The document provides an evaluation of the impacts of the goDCgo transportation program between July 2014 and June 2015. It summarizes that through various services including ride matching, employer outreach, bikeshare promotion, and a website, goDCgo helped over 33,000 people shift to alternative transportation modes. This resulted in a reduction of over 33,000 vehicle trips and 509,000 vehicle miles traveled daily. The travel changes also reduced daily emissions and saved over 18,700 gallons of fuel per day.
goDCgo implemented new programs and tactics over the past year to expand their reach. They launched residential and university services programs to promote sustainable transportation to those groups. Their employer services program continued successfully promoting options like Capital Bikeshare and Circulator. goDCgo hosted various events, grew partnerships, and saw increases in metrics like new employers enrolled and transportation benefit programs.
This document summarizes a study that developed an index to measure satisfaction with public transport using fuzzy clustering. The researchers applied a fuzzy clustering method called the Grade of Membership (GoM) model to data from a public transport user satisfaction survey in Lisbon, Portugal. This allowed them to represent user satisfaction along a single dimension or index, accounting for the multidimensional nature of satisfaction. They then used regression tree analysis to identify factors that influence satisfaction levels according to the index. The study aims to provide public transport operators with insights into user satisfaction to help improve services and encourage greater use of public transportation.
A travel behaviour change framework for the city of Cape Town.pdfSamantha Martinez
This document proposes a theoretical framework to guide the city of Cape Town's travel demand management (TDM) strategy. It categorizes different TDM measures and analyzes their observed impacts based on international evidence. Financial charges, like road pricing, are found to reduce single-occupancy vehicle use the most, by around 15% on average. Financial incentives and regulatory measures reduce use by around 5% each, while voluntary measures reduce use by around 2%. The proposed framework explains how to package, sequence, target and implement TDM measures strategically to achieve the city's goal of reducing single-occupancy vehicle traffic into the city center by 10% over five years.
The document discusses various methods for evaluating transportation alternatives and projects. It describes evaluating projects based on their costs and benefits. Key points:
- Transportation projects are evaluated based on criteria like capital costs, maintenance costs, travel time savings, safety improvements, and environmental impacts.
- Common evaluation methods include calculating the present worth, equivalent uniform annual cost, benefit-cost ratio, and internal rate of return to compare project alternatives.
- The evaluation helps decision makers determine if the benefits of a project justify the costs and informs which alternative best meets objectives like reduced travel times or increased safety.
The document discusses measures that can be taken to influence a modal shift from private cars to public transport in order to reduce traffic congestion in a city. It recommends conducting a stated preference survey to understand factors that influence travel choices. It also suggests implementing policies to dissuade car use such as prioritizing public transit at traffic signals, improving reliability and travel times of public transport, and providing more real-time transit information for passengers. Safety improvements for pedestrians are also highlighted.
Transport has a major impact on the quality of life in a city, its environment and the economy. Transport Authorities globally are facing similar strategic challenges around worsening congestion, insufficient transport infrastructure, affordability constraints, increasing emissions and growing customer needs...
Multi-Dimensional Framework in Public Transport PlanningSharu Gangadhar
This document proposes a three-layer model for a multi-dimensional evaluation framework to measure public transit service performance. The framework uses both subjective and objective measures, allowing input from stakeholders. It is built on a three-tier architecture for flexibility, a good user interface, and resistance to change. The three tiers are the system level, route level, and data storage level. The framework evaluates performance at different levels of detail using criteria, indicators, and a weighting process to reflect importance.
goDCgo implemented new programs and tactics over the past year to expand their reach. They launched residential and university services programs to promote sustainable transportation to those groups. Their employer services program continued successfully promoting options like Capital Bikeshare and Circulator. goDCgo hosted various events, grew partnerships, and saw increases in metrics like new employers enrolled and transportation benefit programs.
This document summarizes a study that developed an index to measure satisfaction with public transport using fuzzy clustering. The researchers applied a fuzzy clustering method called the Grade of Membership (GoM) model to data from a public transport user satisfaction survey in Lisbon, Portugal. This allowed them to represent user satisfaction along a single dimension or index, accounting for the multidimensional nature of satisfaction. They then used regression tree analysis to identify factors that influence satisfaction levels according to the index. The study aims to provide public transport operators with insights into user satisfaction to help improve services and encourage greater use of public transportation.
A travel behaviour change framework for the city of Cape Town.pdfSamantha Martinez
This document proposes a theoretical framework to guide the city of Cape Town's travel demand management (TDM) strategy. It categorizes different TDM measures and analyzes their observed impacts based on international evidence. Financial charges, like road pricing, are found to reduce single-occupancy vehicle use the most, by around 15% on average. Financial incentives and regulatory measures reduce use by around 5% each, while voluntary measures reduce use by around 2%. The proposed framework explains how to package, sequence, target and implement TDM measures strategically to achieve the city's goal of reducing single-occupancy vehicle traffic into the city center by 10% over five years.
The document discusses various methods for evaluating transportation alternatives and projects. It describes evaluating projects based on their costs and benefits. Key points:
- Transportation projects are evaluated based on criteria like capital costs, maintenance costs, travel time savings, safety improvements, and environmental impacts.
- Common evaluation methods include calculating the present worth, equivalent uniform annual cost, benefit-cost ratio, and internal rate of return to compare project alternatives.
- The evaluation helps decision makers determine if the benefits of a project justify the costs and informs which alternative best meets objectives like reduced travel times or increased safety.
The document discusses measures that can be taken to influence a modal shift from private cars to public transport in order to reduce traffic congestion in a city. It recommends conducting a stated preference survey to understand factors that influence travel choices. It also suggests implementing policies to dissuade car use such as prioritizing public transit at traffic signals, improving reliability and travel times of public transport, and providing more real-time transit information for passengers. Safety improvements for pedestrians are also highlighted.
Transport has a major impact on the quality of life in a city, its environment and the economy. Transport Authorities globally are facing similar strategic challenges around worsening congestion, insufficient transport infrastructure, affordability constraints, increasing emissions and growing customer needs...
Multi-Dimensional Framework in Public Transport PlanningSharu Gangadhar
This document proposes a three-layer model for a multi-dimensional evaluation framework to measure public transit service performance. The framework uses both subjective and objective measures, allowing input from stakeholders. It is built on a three-tier architecture for flexibility, a good user interface, and resistance to change. The three tiers are the system level, route level, and data storage level. The framework evaluates performance at different levels of detail using criteria, indicators, and a weighting process to reflect importance.
1. A visitor learns about the program through digital ads or a community event. They visit the website, review information, and decide to join by completing primary consent.
2. Another visitor learns from their doctor and completes primary consent during a clinic visit. They later log into the participant portal to finish enrollment steps like surveys and sample collection.
3. A third visitor sees social media posts. Intrigued, they explore the website, learn about benefits to research, and fully commit by providing consent, surveys, and a bio-sample to become a core participant.
Brown-Lee_How-Does-Modeling-and-Forecasting-Support-Performance-Based-PlanningColby Brown
The document summarizes the results of a survey of 100 MPOs (Metropolitan Planning Organizations) regarding their use of modeling, forecasting, and performance measures in transportation planning. Key findings include:
- Most MPOs are using performance measures to develop long-range transportation plans, especially mobility measures.
- MPOs that use performance measures are more likely to evaluate alternative scenarios and use travel demand models.
- Conventional "four-step" models are most common, while activity-based models are slightly more common for MPOs using performance measures.
- MPOs feel their models can accurately forecast transportation performance but are less comfortable sharing performance data publicly.
Roadside surveys provide important baseline data for identifying road safety problems and measuring the effectiveness of countermeasures. They include transport surveys to understand how the transportation system functions and meets demand. Supply and demand surveys examine both the infrastructure and how transportation is used. Operator, driver, passenger and household surveys provide insight into issues like vehicle productivity, travel patterns, and the socioeconomic impacts of accidents. Traffic counts are also important to understand traffic volumes, vehicle types, and speeds in different areas. Together, these surveys pinpoint safety risks and help develop appropriate solutions.
Smart Commute Evaluation: Tools, Techniques and Lessons Learned in Monitoring...Smart Commute
Smart Commute works with stakeholders to reduce traffic and emissions through workplace transportation demand management programs. It has expanded from an initial pilot project in 2001 to involve multiple municipalities and partners across the Greater Toronto Area. Evaluation of these programs involves monitoring activities, impacts, and customer satisfaction to track progress, justify funding, and improve services over time. Challenges include balancing implementation priorities with thorough evaluation and ensuring standardized data collection while allowing for flexibility. Ongoing efforts focus on refining monitoring tools and using lessons learned to strengthen evaluation.
Max sumo and maxeva train the trainer meetingFriso Metz
The document outlines an agenda for a training meeting on MaxSumo and MaxEva tools. It introduces MaxSumo as a tool for planning, monitoring, and evaluating sustainable mobility management projects, and describes its key features and steps. It then provides an overview of MaxEva, a database for entering MaxSumo project data that has been improved based on feedback. The agenda includes sessions on using MaxSumo and MaxEva, entering projects into MaxEva, and benchmarking projects.
Presentation by Tom Worsley, Visiting Research Fellow, delivered as part of the annual series of Beesley lectures, organised by the Institute of Economic Affairs at the Institute of Directors in London.
This document presents a proposed end-to-end tour system that uses machine learning and recommendation algorithms. The system would allow users to take a quiz to receive personalized tour destination recommendations. It would also provide pre-packaged tour options and allow users to customize their own tours by selecting different components. The system is divided into three modules: a prediction quiz, a recommendation system using singular value decomposition, and a customization module. The goal is to make the tour planning process easy and hassle-free for users.
1. The document is notes written by Saqib Imran, a civil engineering student in Peshawar, Pakistan, for other students and engineers.
2. It covers topics related to traffic and transportation engineering, including highway engineering, traffic simulation software, trip distribution models, and factors affecting trip generation in traffic studies.
3. Key concepts discussed include calibration and validation of traffic simulation models, gravity and growth factor trip distribution models, and how trip purpose, time of travel, transportation mode, route, and utility influence trip generation.
[2015 e-Government Program] Action Plan : Quito(Ecuador)shrdcinfo
The document outlines an improvement strategy and action plan to modernize public transportation systems in level 1 cities in Ecuador. It aims to establish integrated transportation through 5 phases: 1) preparing infrastructure, 2) collecting data, 3) analyzing data, 4) automating payment methods, and 5) expanding the program. The expected results are increased economic productivity, improved technical efficiency of transportation, and social/environmental benefits like decreased pollution and increased safety. Challenges include obtaining foreign investment and changing public perceptions, but solutions involve showcasing financial benefits to investors and an informative public campaign.
MODE CHOICE ANALYSIS BETWEEN ONLINE RIDE-HAILING AND PARATRANSIT IN BANJARMAS...IAEME Publication
Nowadays, application-based transportation is in great demand by the public. Public transport users began to switch to private-hire transportation. This is influenced by the increasingly sophisticated communication tools, making it’s easier for people to mobilise. Another reason for the large number of private-hire transportation use is due to the disappointment that arises on the insufficient of public transportation facilities. This raises the competition between both transporation modes—providing people with a choice in choosing the most appropriate one to use in supporting their activities. The aim of this research is to get a model that can explain the probability of Banjarmasin people’s preference in choosing between online ride-hailing and paratransit in banjarmasin city. In addition to this, the research also aims to find out about the factors or attributes that affect their preference, analyze the influence of travel costs and other attributes reviewed from different travel distance. This research was analyzed using multinomial logite method in Limdep Nlogit 4.0 software. Further analysis is done by multinomial logic analysis to obtain utility and probability on the transportation preference. After obtaining the best utility model,the data on cost sensitivity, travel time and convenience in choosing between both type of transporation, were obtained. Based on the result, the researcher found that the greater the cost and the travel time are, the greater the probability for the private-hire transportation to be choosen will be. Another aspect that makes people tend to choose private-hire transporation rather then public one is the convenience. The result shows that even if public transporation tries to improve its convenience from what it has now, it won’t cause any significant change on the number. In conclusion, private-hire transportation has the highest probability value compared to others. Based on the sensitivity of convenience, then it can be concluded that if the convenience of public transport is improved from its existing conditions, the probability may increase but the raise in the probability value isn’t going to be significant. Therefore, the largest probability value of the three modes belongs to online taxibike.
Clear Air Zones – What are Local Authorities Proposing? - Nigel BellamyIES / IAQM
The document summarizes progress on Clean Air Zones in the UK. It outlines that the UK has been in breach of legal limits for nitrogen dioxide and discusses the need for immediate action to improve air quality and health. It defines Clean Air Zones as areas with restrictions on certain vehicles to encourage cleaner vehicles. Authorities need to develop local plans with measures to achieve compliance, which requires modeling emissions and impacts. Options being considered by authorities include charges for different vehicle types in Clean Air Zones of varying sizes and stringency. Authorities are at different stages with some publishing initial plans focusing on buses, taxis, HGVs or LGVs. The overall progress aims to achieve compliance with legal limits as soon as possible to reduce human exposure
This document discusses using system dynamics modeling to analyze the business dynamics of ride-hailing services. It describes combining system dynamics modeling with performance management to gain a dynamic, non-linear perspective on complex ride-hailing businesses. Several system dynamics performance management models are presented that capture causal relationships and simulate strategy impacts over time. Key aspects modeled include strategic resources, value drivers, end results, customer and driver perceptions, and pricing dynamics.
A Renaissance Planning presentation on mobility fees. Mobility fees are a transportation system charge on development that allows local governments to assess the proportionate cost of transportation improvements needed to serve the demand generated by new development projects. Whereas older methods of charging developers only allow for specific roadway improvement, mobility fees allow for funding transit and other multi-modal improvements.
Constructing a musa model to determine priority factor of a servperf model.Alexander Decker
This document summarizes a study that aimed to determine the priority factor among factors in a SERVPERF model for measuring student satisfaction with hostel management services. The researchers attempted to build a Multi-criteria Satisfaction Analysis (MUSA) model based on ordinal regression and linear programming to identify the priority factor. However, the MUSA model built was found to be unstable and unable to interpret the dataset. The researchers were unable to construct a stable and interpretable MUSA model for this data, even when categorizing the data in various ways.
Constructing a musa model to determine priority factor of a servperf model.Alexander Decker
This document summarizes a study that aimed to determine the priority factor among factors in a SERVPERF model for measuring student satisfaction with hostel management services. The researchers attempted to build a Multi-criteria Satisfaction Analysis (MUSA) model based on ordinal regression and linear programming to identify the priority factor. However, the MUSA model built was found to be unstable and unable to interpret the dataset. The researchers were unable to construct a stable MUSA model even after categorizing the data by gender and home state.
The transportation planning is considering a large denstial things l.pdfannamalaicells
The transportation planning is considering a large denstial things like trip generation to its
design, it never shows or followed any single result after the precise evluation. That is due to
following reason-
Transportation helps shape an area’s economic health and quality of life. Not only does the
transportation system provide for the mobility of people and goods, it also influences patterns of
growth and economic activity by providing access to land. The performance of the system affects
public policy concerns like air quality, environmental resource consumption, social equity, land
use, urban growth, economic development, safety, and security. Transportation planning
recognizes the critical links between transportation and other societal goals. The planning
process is more than merely listing highway and transit capital projects. It requires developing
strategies for operating, managing, maintaining, and financing the area’s transportation system in
such a way as to advance the area’s long-term goals. In all the aspects we considered a average
calculation for more betterment.
Air Quality- In it we also calculate the air monitering devices and mobile/area pollution
producers, in order to it we design the next transporting data.
Congestion Management Process (CMP) -Increase the frequency or severity of existing
violations of the standards that is also issue in planning.
Financial Planning and Programming - this is a main factor that influence the planning due to
more negotiation between local bodied.
Freight Movement - More availability to the road and vehicle should be always under
consideration.
Land Use and Transportation- The Right to Way is always a facctor for proper construction.
Performance Measures - duration and durability behaves as a standard.
Planning and Environment Linkages
Public Involvement - how it is useful for public and how much for government body is always a
factor.
Safety - Very neccesary topic in planning purpose.
Security
System Management and Operations (M&O)
Technology Applications for Planning: Models, GIS, and Visualization
Title VI/Environmental Justice (EJ)
Transportation Asset Management
Other local transpotation link and data
Solution
The transportation planning is considering a large denstial things like trip generation to its
design, it never shows or followed any single result after the precise evluation. That is due to
following reason-
Transportation helps shape an area’s economic health and quality of life. Not only does the
transportation system provide for the mobility of people and goods, it also influences patterns of
growth and economic activity by providing access to land. The performance of the system affects
public policy concerns like air quality, environmental resource consumption, social equity, land
use, urban growth, economic development, safety, and security. Transportation planning
recognizes the critical links between transportation and other societal goals. The planning
process is mo.
The Municipal Reference Model provides the Business Architecture for Government, based on an outside-in, citizen-centred view, in which the business of government is defined by the programs and services that it provides to citizens.
Future mobility strategy summary consultation 2020Farah Tam
The document presents a draft strategy for future mobility in West Yorkshire. It discusses developing a strategy in collaboration with partners to explore how innovation and new technologies can help meet regional goals. The strategy establishes 8 principles and identifies 5 themes - digital demand responsive transport, shared transport, mobility as a service, connected and autonomous vehicles, and first/last mile freight. It proposes short, medium and long term actions to support the themes and achieve objectives of inclusive growth, zero carbon emissions, and improved transport.
The Aggressive Driving and Road Rage Abolishers have established a program to combat aggressive driving and road rage in New Jersey. Their goal is to produce a continued driver education course and safety video to raise awareness about safe driving and reduce accidents. They will collect data on highways with high accident rates to implement their program and measure its impact by comparing accident rates in areas with and without the program. Their budget request is $20,000 to develop a program website.
Creating Better Places with Transportation Demand Management (TDM)Mobility Lab
A “transit premium” can increase property values by anywhere between a few percentage points up to more than 150 percent.
TDM focuses on shifting travelers away from single occupancy-vehicle modes like biking, walking, bus, and rail. In many cases, however, TDM solutions and programs may address only a single alternative mode, or ignore the increasing diversity in how people – particularly younger generations – are traveling.
There is strong evidence of this narrow focus occurring frequently. Residential buildings may tout their WalkScore as a measure of pedestrian-friendliness. Or a commercial building may earn a Bicycle Friendly Business’ designation from the League of American Bicyclists. While these tools and designations are certainly valuable, sustainable buildings should have an an equitable distribution of transportation options and opportunities.
Most property owners and managers (and the business leaders who operate within them) can find ways to better promote and encourage a range of multi-modal options.
My contribution to helping them do so is the Multi-Modal Transportation Score (or what I like to call ModeScore for short). It measures the total accessibility of a given building, taking into account all possible sustainable transportation modes. My overarching goal is that building users will create and embrace programs to encourage and increase alternative travel.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
1. A visitor learns about the program through digital ads or a community event. They visit the website, review information, and decide to join by completing primary consent.
2. Another visitor learns from their doctor and completes primary consent during a clinic visit. They later log into the participant portal to finish enrollment steps like surveys and sample collection.
3. A third visitor sees social media posts. Intrigued, they explore the website, learn about benefits to research, and fully commit by providing consent, surveys, and a bio-sample to become a core participant.
Brown-Lee_How-Does-Modeling-and-Forecasting-Support-Performance-Based-PlanningColby Brown
The document summarizes the results of a survey of 100 MPOs (Metropolitan Planning Organizations) regarding their use of modeling, forecasting, and performance measures in transportation planning. Key findings include:
- Most MPOs are using performance measures to develop long-range transportation plans, especially mobility measures.
- MPOs that use performance measures are more likely to evaluate alternative scenarios and use travel demand models.
- Conventional "four-step" models are most common, while activity-based models are slightly more common for MPOs using performance measures.
- MPOs feel their models can accurately forecast transportation performance but are less comfortable sharing performance data publicly.
Roadside surveys provide important baseline data for identifying road safety problems and measuring the effectiveness of countermeasures. They include transport surveys to understand how the transportation system functions and meets demand. Supply and demand surveys examine both the infrastructure and how transportation is used. Operator, driver, passenger and household surveys provide insight into issues like vehicle productivity, travel patterns, and the socioeconomic impacts of accidents. Traffic counts are also important to understand traffic volumes, vehicle types, and speeds in different areas. Together, these surveys pinpoint safety risks and help develop appropriate solutions.
Smart Commute Evaluation: Tools, Techniques and Lessons Learned in Monitoring...Smart Commute
Smart Commute works with stakeholders to reduce traffic and emissions through workplace transportation demand management programs. It has expanded from an initial pilot project in 2001 to involve multiple municipalities and partners across the Greater Toronto Area. Evaluation of these programs involves monitoring activities, impacts, and customer satisfaction to track progress, justify funding, and improve services over time. Challenges include balancing implementation priorities with thorough evaluation and ensuring standardized data collection while allowing for flexibility. Ongoing efforts focus on refining monitoring tools and using lessons learned to strengthen evaluation.
Max sumo and maxeva train the trainer meetingFriso Metz
The document outlines an agenda for a training meeting on MaxSumo and MaxEva tools. It introduces MaxSumo as a tool for planning, monitoring, and evaluating sustainable mobility management projects, and describes its key features and steps. It then provides an overview of MaxEva, a database for entering MaxSumo project data that has been improved based on feedback. The agenda includes sessions on using MaxSumo and MaxEva, entering projects into MaxEva, and benchmarking projects.
Presentation by Tom Worsley, Visiting Research Fellow, delivered as part of the annual series of Beesley lectures, organised by the Institute of Economic Affairs at the Institute of Directors in London.
This document presents a proposed end-to-end tour system that uses machine learning and recommendation algorithms. The system would allow users to take a quiz to receive personalized tour destination recommendations. It would also provide pre-packaged tour options and allow users to customize their own tours by selecting different components. The system is divided into three modules: a prediction quiz, a recommendation system using singular value decomposition, and a customization module. The goal is to make the tour planning process easy and hassle-free for users.
1. The document is notes written by Saqib Imran, a civil engineering student in Peshawar, Pakistan, for other students and engineers.
2. It covers topics related to traffic and transportation engineering, including highway engineering, traffic simulation software, trip distribution models, and factors affecting trip generation in traffic studies.
3. Key concepts discussed include calibration and validation of traffic simulation models, gravity and growth factor trip distribution models, and how trip purpose, time of travel, transportation mode, route, and utility influence trip generation.
[2015 e-Government Program] Action Plan : Quito(Ecuador)shrdcinfo
The document outlines an improvement strategy and action plan to modernize public transportation systems in level 1 cities in Ecuador. It aims to establish integrated transportation through 5 phases: 1) preparing infrastructure, 2) collecting data, 3) analyzing data, 4) automating payment methods, and 5) expanding the program. The expected results are increased economic productivity, improved technical efficiency of transportation, and social/environmental benefits like decreased pollution and increased safety. Challenges include obtaining foreign investment and changing public perceptions, but solutions involve showcasing financial benefits to investors and an informative public campaign.
MODE CHOICE ANALYSIS BETWEEN ONLINE RIDE-HAILING AND PARATRANSIT IN BANJARMAS...IAEME Publication
Nowadays, application-based transportation is in great demand by the public. Public transport users began to switch to private-hire transportation. This is influenced by the increasingly sophisticated communication tools, making it’s easier for people to mobilise. Another reason for the large number of private-hire transportation use is due to the disappointment that arises on the insufficient of public transportation facilities. This raises the competition between both transporation modes—providing people with a choice in choosing the most appropriate one to use in supporting their activities. The aim of this research is to get a model that can explain the probability of Banjarmasin people’s preference in choosing between online ride-hailing and paratransit in banjarmasin city. In addition to this, the research also aims to find out about the factors or attributes that affect their preference, analyze the influence of travel costs and other attributes reviewed from different travel distance. This research was analyzed using multinomial logite method in Limdep Nlogit 4.0 software. Further analysis is done by multinomial logic analysis to obtain utility and probability on the transportation preference. After obtaining the best utility model,the data on cost sensitivity, travel time and convenience in choosing between both type of transporation, were obtained. Based on the result, the researcher found that the greater the cost and the travel time are, the greater the probability for the private-hire transportation to be choosen will be. Another aspect that makes people tend to choose private-hire transporation rather then public one is the convenience. The result shows that even if public transporation tries to improve its convenience from what it has now, it won’t cause any significant change on the number. In conclusion, private-hire transportation has the highest probability value compared to others. Based on the sensitivity of convenience, then it can be concluded that if the convenience of public transport is improved from its existing conditions, the probability may increase but the raise in the probability value isn’t going to be significant. Therefore, the largest probability value of the three modes belongs to online taxibike.
Clear Air Zones – What are Local Authorities Proposing? - Nigel BellamyIES / IAQM
The document summarizes progress on Clean Air Zones in the UK. It outlines that the UK has been in breach of legal limits for nitrogen dioxide and discusses the need for immediate action to improve air quality and health. It defines Clean Air Zones as areas with restrictions on certain vehicles to encourage cleaner vehicles. Authorities need to develop local plans with measures to achieve compliance, which requires modeling emissions and impacts. Options being considered by authorities include charges for different vehicle types in Clean Air Zones of varying sizes and stringency. Authorities are at different stages with some publishing initial plans focusing on buses, taxis, HGVs or LGVs. The overall progress aims to achieve compliance with legal limits as soon as possible to reduce human exposure
This document discusses using system dynamics modeling to analyze the business dynamics of ride-hailing services. It describes combining system dynamics modeling with performance management to gain a dynamic, non-linear perspective on complex ride-hailing businesses. Several system dynamics performance management models are presented that capture causal relationships and simulate strategy impacts over time. Key aspects modeled include strategic resources, value drivers, end results, customer and driver perceptions, and pricing dynamics.
A Renaissance Planning presentation on mobility fees. Mobility fees are a transportation system charge on development that allows local governments to assess the proportionate cost of transportation improvements needed to serve the demand generated by new development projects. Whereas older methods of charging developers only allow for specific roadway improvement, mobility fees allow for funding transit and other multi-modal improvements.
Constructing a musa model to determine priority factor of a servperf model.Alexander Decker
This document summarizes a study that aimed to determine the priority factor among factors in a SERVPERF model for measuring student satisfaction with hostel management services. The researchers attempted to build a Multi-criteria Satisfaction Analysis (MUSA) model based on ordinal regression and linear programming to identify the priority factor. However, the MUSA model built was found to be unstable and unable to interpret the dataset. The researchers were unable to construct a stable and interpretable MUSA model for this data, even when categorizing the data in various ways.
Constructing a musa model to determine priority factor of a servperf model.Alexander Decker
This document summarizes a study that aimed to determine the priority factor among factors in a SERVPERF model for measuring student satisfaction with hostel management services. The researchers attempted to build a Multi-criteria Satisfaction Analysis (MUSA) model based on ordinal regression and linear programming to identify the priority factor. However, the MUSA model built was found to be unstable and unable to interpret the dataset. The researchers were unable to construct a stable MUSA model even after categorizing the data by gender and home state.
The transportation planning is considering a large denstial things l.pdfannamalaicells
The transportation planning is considering a large denstial things like trip generation to its
design, it never shows or followed any single result after the precise evluation. That is due to
following reason-
Transportation helps shape an area’s economic health and quality of life. Not only does the
transportation system provide for the mobility of people and goods, it also influences patterns of
growth and economic activity by providing access to land. The performance of the system affects
public policy concerns like air quality, environmental resource consumption, social equity, land
use, urban growth, economic development, safety, and security. Transportation planning
recognizes the critical links between transportation and other societal goals. The planning
process is more than merely listing highway and transit capital projects. It requires developing
strategies for operating, managing, maintaining, and financing the area’s transportation system in
such a way as to advance the area’s long-term goals. In all the aspects we considered a average
calculation for more betterment.
Air Quality- In it we also calculate the air monitering devices and mobile/area pollution
producers, in order to it we design the next transporting data.
Congestion Management Process (CMP) -Increase the frequency or severity of existing
violations of the standards that is also issue in planning.
Financial Planning and Programming - this is a main factor that influence the planning due to
more negotiation between local bodied.
Freight Movement - More availability to the road and vehicle should be always under
consideration.
Land Use and Transportation- The Right to Way is always a facctor for proper construction.
Performance Measures - duration and durability behaves as a standard.
Planning and Environment Linkages
Public Involvement - how it is useful for public and how much for government body is always a
factor.
Safety - Very neccesary topic in planning purpose.
Security
System Management and Operations (M&O)
Technology Applications for Planning: Models, GIS, and Visualization
Title VI/Environmental Justice (EJ)
Transportation Asset Management
Other local transpotation link and data
Solution
The transportation planning is considering a large denstial things like trip generation to its
design, it never shows or followed any single result after the precise evluation. That is due to
following reason-
Transportation helps shape an area’s economic health and quality of life. Not only does the
transportation system provide for the mobility of people and goods, it also influences patterns of
growth and economic activity by providing access to land. The performance of the system affects
public policy concerns like air quality, environmental resource consumption, social equity, land
use, urban growth, economic development, safety, and security. Transportation planning
recognizes the critical links between transportation and other societal goals. The planning
process is mo.
The Municipal Reference Model provides the Business Architecture for Government, based on an outside-in, citizen-centred view, in which the business of government is defined by the programs and services that it provides to citizens.
Future mobility strategy summary consultation 2020Farah Tam
The document presents a draft strategy for future mobility in West Yorkshire. It discusses developing a strategy in collaboration with partners to explore how innovation and new technologies can help meet regional goals. The strategy establishes 8 principles and identifies 5 themes - digital demand responsive transport, shared transport, mobility as a service, connected and autonomous vehicles, and first/last mile freight. It proposes short, medium and long term actions to support the themes and achieve objectives of inclusive growth, zero carbon emissions, and improved transport.
The Aggressive Driving and Road Rage Abolishers have established a program to combat aggressive driving and road rage in New Jersey. Their goal is to produce a continued driver education course and safety video to raise awareness about safe driving and reduce accidents. They will collect data on highways with high accident rates to implement their program and measure its impact by comparing accident rates in areas with and without the program. Their budget request is $20,000 to develop a program website.
Creating Better Places with Transportation Demand Management (TDM)Mobility Lab
A “transit premium” can increase property values by anywhere between a few percentage points up to more than 150 percent.
TDM focuses on shifting travelers away from single occupancy-vehicle modes like biking, walking, bus, and rail. In many cases, however, TDM solutions and programs may address only a single alternative mode, or ignore the increasing diversity in how people – particularly younger generations – are traveling.
There is strong evidence of this narrow focus occurring frequently. Residential buildings may tout their WalkScore as a measure of pedestrian-friendliness. Or a commercial building may earn a Bicycle Friendly Business’ designation from the League of American Bicyclists. While these tools and designations are certainly valuable, sustainable buildings should have an an equitable distribution of transportation options and opportunities.
Most property owners and managers (and the business leaders who operate within them) can find ways to better promote and encourage a range of multi-modal options.
My contribution to helping them do so is the Multi-Modal Transportation Score (or what I like to call ModeScore for short). It measures the total accessibility of a given building, taking into account all possible sustainable transportation modes. My overarching goal is that building users will create and embrace programs to encourage and increase alternative travel.
Similar to goDCgo Year 4 Annual Impact Report (20)
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goDCgo Year 4 Annual Impact Report
1. GODCGO TRANSPORTATION PROGRAM
2014 IMPACT EVALUATION – SUMMARY REPORT
JULY 2014 – JUNE 2015
PREPARED BY:
LDA CONSULTING
WASHINGTON, DC, 202-548-0205
JULY 18, 2015
2. goDCgo – July 2014-June 2015 Program Impact Report
i
TABLE OF CONTENTS
SECTION 1 – EVALUATION METHOD 1
OVERVIEW 1
PERFORMANCE INDICATORS 1
IMPACT CALCULATION APPROACH 3
SECTION 2 – 2014 PROGRAM IMPACTS 6
IMPACT SUMMARY 6
SERVICES INCLUDED IN THE EVALUATION 6
FACTORS USED IN THE CALCULATION 8
APPENDICES 10
2014 Impact Calculation Worksheets
1-a Calculation Factors 11
1-b Vehicle Trip and VMT Impacts – Calculation by Service 12
1-c Service Overlap Factors 13
1-d Summary of Program Impacts 14
1-e Notes on Data Sources 15
3. goDCgo – July 2014-June 2015 Program Impact Report
1
SECTION 1 – EVALUATION METHOD
Overview
In early 2010, the District of Columbia Department of Transportation (DDOT) initiated the goDCgo program to
provide travel information and assistance services to residents, employees, and visitors of the District of Columbia.
The program offers a variety of Transportation Demand Management (TDM) services designed to reduce reliance
on single-occupant vehicles for travel. TDM actions can facilitate and encourage use of non-drive alone “shared
ride” travel options such as carpooling, vanpooling, and public transit or non-motorized transportation options,
such as biking or walking. TDM actions such as telework and compressed work schedules can enable travelers to
avoid a trip entirely or shift the time the trip is made to a less congested time of day.
This report documents reductions in vehicle trips, vehicle miles traveled, vehicle emissions, and energy use
generated through use of goDCgo services from July 2014 through June 2015.
Performance Indicators
goDCgo is charged with documenting the results of its services. The evaluation system developed for the goDCgo
program defines performance by a progression of actions that track with the behavior transformation continuum
typically applied to social marketing models:
Awareness Build initial awareness of options/concept
Familiarity Increase appreciation and understanding of specific options
Consideration/Trial Try one or more options/have a favorable experience
Desired behavior Adopt the behavior in everyday living
The goDCgo impact evaluation adapts this model for a seven-
step “continuum” of results. The first five steps mirror the social
behavioral change model described above. The sixth category
assesses the factors influencing the behavioral changes. The
final category defines external impacts resulting from the
behavior changes. The 2014 goDCgo evaluation estimates
transportation and emission impacts, but future evaluations
also could include other personal or social impacts, such as
enhanced quality of life, personal travel savings, and other
outcomes or benefits of travel behavior changes.
Travel Behavior Change Continuum
1) Awareness of modes/TDM services
2) Attitudes toward modes, willingness to try new mode
3) Participation in services
4) Satisfaction with services and repeated use
5) Utilization of modes, travel changes
6) Influences on decisions to change
7) Impacts from travel changes
The primary focus of this report is category 7, Program impacts, but indicators in categories 3 (Participation), 5
(Utilization), 6 (Influences), also are relevant to this report, as they are used as components in the calculation
impacts. Following are brief explanations of each category and typical sources of data for a TDM program
evaluation.
4. goDCgo – July 2014-June 2015 Program Impact Report
2
Participation (category 3) – Program participation refers to the number of customers who receive a TDM
services, for example, the numbers of employees at employer client sites or the number of GoDCGo.com
website users. Participation data are typically captured through program tracking.
Mode Utilization / Travel Change (category 5) – In the context of TDM performance, travel change refers to
changes customers make in how, when, or where they travel as a result of the services they received. In this
evaluation travel changes are characterized by three indicators:
1) Temporary placement rate – percentage of service users who tried a new travel mode after receiving
a service, but did not continue using it. A related element is the duration of the new travel
arrangement—how long did the travel change last?
2) Continued placement rate – percentage of service users who made a travel change and continued the
change
3) Alternative mode placements – the number of service users who shifted to a non-drive alone mode
These indicators are assessed by surveying a sample of the targeted population to ask about their travel
patterns during the evaluation period and identify commuters who made a travel change.
Influence on Change (category 6) – Because many factors influence travel behavior, the evaluation also
examines the role the service played in influencing the travel change. Influence typically is assessed from
survey questions that ask, “Did the “X” service encourage or assist you to make this change?”
Impacts (category 7) – The final set of performance indicators represent the contribution of the services to
regional travel and air quality objectives, including:
1) Vehicle Trip Reduction – Measure of reduced single-occupant travel—e.g., “cars off the road.” This is
typically measured by surveying a sample of service users about their current travel and their travel
before they used the service. These survey data are used to derive a multiplier factor that represents
the average number of trips reduced per user.
2) Vehicle Miles Traveled (VMT) Reduction – A second measure of reduced single-occupant mileage,
either by vehicle trips eliminated or reduced length of existing vehicle trips. VMT reduction also is
typically measured through a survey of service users. In this case, survey data are used to derive a
multiplier factor for the average miles per trip reduced.
3) Emission Reduction – Reductions in various pollutants emitted by vehicles. For the goDCgo
evaluation, this impact is calculated by multiplying the vehicle trips reduced and VMT reduced by
emission factors that are specific to the Washington metropolitan region.
4) Energy Savings – Reduction in fuel used for travel purposes. This impact also is calculated using a
multiplier factor related to the average fuel economy of the region’s vehicle fleet.
5. goDCgo – July 2014-June 2015 Program Impact Report
3
The factors noted above are applied in the impact calculation methodology to calculate TDM program impacts
resulting from commuters’ travel changes. These calculations are briefly described below. Section 2, which
presents the results of the impact calculation, explains specifically how this basic approach was implemented in
the goDCgo evaluation.
Impact Calculation Approach
Figure 1 illustrates the method developed to calculate travel and air quality impacts for goDCgo services. It consists
of a series of multiplication steps beginning with a definition of the population base for the service. A series of
multiplier factors derived from a survey of users are then applied to the population base to calculate service
impacts. This method was applied for each goDCgo service for which participation could be tracked and multiplier
factors could be developed. Each service has a unique set of factors, depending on the characteristics of the
service and users, but the basic calculation method is the same for all services.
Figure 1: Impact Calculation Multipliers Series
A brief description of each of the steps is presented below.
1. Estimate commuter population “base” for the service
A TDM service is designed to influence or encourage a targeted set of travelers to shift to non-drive alone
modes. These travelers / customers / service users represent the population base for that service, for
example, the population of goDCgo.com website users. Population base estimates were identified for each
service from goDCgo data.
2. Estimate “placement rate” and “influenced placement rate”
Placement rate refers to the percentage of the population base “placed” in an alternative mode after receiving
a service. Placement rates are typically estimated from survey data of a sample of the population and vary
from one service to another, depending on the characteristics of the service and population. To collect
placement rate data, service users are asked several questions:
Target / User Population
e.g. goDCgo.com website users
X
Placement rate =
X
“Vehicle trip reduction” factor =
X
travel distance =
X
Emission factors =
Vehicle trips reduced by
mode changes
VMT reduced by
mode changes
Emissions reduced by
mode changes
Participants who made travel change
influenced by service - “Placements”
6. goDCgo – July 2014-June 2015 Program Impact Report
4
How do you travel now—what modes do you use and how often do you use them?
Did you make any changes in your travel since you received “X” service?
How did you travel before you received this service?
Did the service encourage or assist you to make this change?
Users who made a travel change are considered “placements.” For each goDCgo service, two rates were
estimated, distinguished by the time the service user used the new mode after shifting. The Continued rate
represents users who shifted to a new alternative mode and continued using the new mode. The Temporary rate
represents users who tried a new alternative mode but returned to original mode within the evaluation period.
Temporary changes are credited only for the duration of time the new mode was used.
The count of commuter placements is additionally
discounted by an “influence factor,” which reflects
the role the service played in influencing or
assisting commuters’ mode change. For example,
commuter surveys show that commuters can be
influenced by many factors to make mode changes,
so it is unrealistic to assume that all mode shifts are
entirely the result of TDM services. This factor is
derived from survey questions that ask, “Did the
“X” service encourage or assist you to make this
change?”
The influence factor also addresses goDCgo’s
contribution in implementing the service. For some
services, such as goDCgo.com, goDCgo is fully
responsible for implementing the program element. But in other cases, such as Capital Bikeshare and DC
Circulator, goDCgo performs a promotional or supporting role, with another entity operating the service. In these
cases, the share of credit assigned to goDCgo is less than 100%.
3. Estimate the number of new alternative mode placements
Step 3 estimates the number of service users who started or increased use of alternative modes as a result of
the service. It was calculated for each service as:
Total Population base (from Step 1) x Placement rate (from Step 2)
4. Estimate the vehicle trip reduction factor for new placements
Next, the vehicle trip reduction (VTR) factor was estimated for each service. The VTR factor is equal to the
average daily vehicle trips reduced per placement, taking into account three types of changes:
1) Shifts to an alternative mode, either from driving alone or from another alternative mode
2) Increased use of alternative modes
3) Increase in the number of riders in an existing carpool or vanpool
The VTR factor combines the trip reduction results of all placements into an average reduction per placement.
Note that shifts from alternative modes to drive alone were not included in the VTR factor, since these
changes are typically unrelated to the services.
7. goDCgo – July 2014-June 2015 Program Impact Report
5
5. Estimate vehicle trips reduced
The number of daily vehicle trips reduced for the service was estimated by multiplying the number of
alternative mode placements by the service’s VTR factor:
Total placements (from Step 3) x VTR factor (from Step 4)
6. Estimate vehicle miles traveled (VMT) reduced
The daily VMT reduced was calculated by multiplying the number of daily vehicle trips reduced (Step 5) by the
average travel distance for service users who made a travel change.
Total vehicle trips reduced (from Step 5) x one-way travel distance
7. Adjust vehicle trips and VMT for access mode
Emission reduction is calculated by multiplying vehicle trips reduced and VMT reduced by emission factors.
But because travelers who drive-alone to a bus stop, train station, or rideshare meeting point create a “cold
start,” the emission reduction analysis subtracts these access trips and the VMT driven to the meeting point
from the vehicle trip and VMT reductions. It is these “adjusted” vehicle trips reduced and VMT reduced, rather
than the initial totals, that are used to calculate emissions reduced.
8. Estimate emissions reduced
Daily emissions reduced by mode shifts were estimated by multiplying regional emission factors by the
number of vehicle trips and VMT reduced. The emissions factors were obtained from the Metropolitan
Washington Council of Governments, for 2015. The emissions factors account for emissions created from a
“cold start,” when a vehicle is first started, a “hot soak,” that occur when the vehicle is later turned off, and
the emissions generated per mile of travel by a warmed-up vehicle.
Vehicle trips reduced (from Step 5) x Trip emission factor
VMT reduced (from Step 7) x VMT / running emission factor
9. Estimate the energy savings
Energy savings is reported as gallons of gasoline saved and was estimated by multiplying the VMT reduced by
an average fuel consumption factor for the regional mix of light duty vehicles.
8. goDCgo – July 2014-June 2015 Program Impact Report
6
SECTION 2 – 2014 PROGRAM IMPACTS
Impact Summary
The services included in the evaluation collectively contributed the following impacts between July 2014 and June
2015. As shown in Table 1, goDCgo helped 33,500 travelers make a travel change, eliminating about 33,260 daily
vehicle trips and 509,000 daily VMT. Each day, their travel changes eliminated 197 kg of NOx, 97 kg of VOC, and
about 184,800 kg of CO2 (greenhouse gases). Finally, these travelers saved more than 18,700 gallons of fuel each
day. Details of the impact calculations are presented in Appendix 1.
Table 1 – goDCgo 2014 Program Impacts and Comparison to 2013 Impacts
Impact Indicator
2014
Impact
2013
Impact
Change
Placements (new alternative mode users) 33,513 32,100 1,413
Daily Vehicle Trips reduced 33,261 32,325 936
Daily Vehicle Miles Traveled reduced 509,046 481,044 28,002
Emissions reduced (daily kilograms)
– Nitrogen Oxides (NOx) 197 186 11
– Volatile Organic Compounds (VOC) 97 93 4
– Carbon Dioxide (greenhouse gases) 184,796 173,931 10,865
Energy savings – daily gallons of fuel saved 18,722 17,608 1,114
These calculations likely represent a conservative estimate of the goDCgo impacts, in that they credit only services
that can be readily documented and count only impacts on commute travel. Additional services, such as tourism
outreach and non-commute resident travel, are not specifically detailed in the calculation, due to lack of data,
although some of their impacts likely are captured under other components. These services will be added in future
years as evaluation data are collected.
Services Included in the Evaluation
The method used to calculate the impacts
described above starts by estimating
individual impacts for each service offered.
To identify the services to be included in
the goDCgo calculation, the consultant
reviewed goDCgo background information
and consulted with goDCgo staff as
needed to obtain a clear understanding of
the activities undertaken in each service,
the target population for each service, and
the performance evaluation data that
were available for the 2014 impact
calculation.
Table 2 lists the services that were included in the 2014 impact calculation. Eight services, Ridematch assistance,
Continued Employer Services, Expanded/New Employer Services, Capital Bikeshare Marketing/Promotion, Bike-
9. goDCgo – July 2014-June 2015 Program Impact Report
7
to-Work Day event promotion, Residential Services, DC Circulator Bus Marketing/Promotion, and goDCgo.com
website, were individually evaluated. Two services, media outreach and goDCgo branding, were assumed to be
“support” services, with their impacts captured through the impacts of directly-estimated services. Note that
Bike-to-Work Day event and Residential Services are new to the evaluation in 2014. They were not evaluated in
previous goDCgo assessments, due to lack of data on behavior change resulting from the services.
Table 2 – Services in 2014 Impact Calculation
Service
Evaluation Level
Primary, Secondary, Support
- Ridematch Assistance (MWCOG database) Primary
- Continued Employer Services (clients in June 2015) Primary
- Expanded / New Employer Services (since July 2014) Primary
- Capital Bikeshare Marketing / Promotion Primary
- Bike-to-Work Day Event Promotion (NEW IN 2014) Primary
- Residential Services (clients in June 2015) (NEW IN 2014) Primary
- DC Circulator Bus Marketing / Promotion Primary
- goDCgo.com Website Secondary
- Media outreach (Facebook, Twitter, blogs, newsletter, ads) Support
- goDCgo branding Support
Table 1 designates an evaluation “level” for each service: primary, secondary, or support. This designation was
established because goDCgo’s services are designed to work together as an attractive package of services. For this
reason, there can be overlap among the programs. For example, a customer could be a Capital Bikeshare member
and use the goDCgo.com website, but the customer should be counted only once in the impact calculation.
The consultants solicited input from goDCgo staff to estimate the degree of overlap between services and
classified each service into one of three categories: primary, secondary, or support. Primary services were defined
as those that were likely to be used alone, or if they were used in combination with other services, were likely to
have the greatest motivational impact of the services being considered.
Secondary services were expected
to be used primarily in
combination with other services
but with less direct influence. The
designation of primary versus
secondary also took into account
how readily data could be
collected on the use and impacts
of the services. Seven of the eight
directly-evaluated services were
designated as primary for the 2014
evaluation. One service,
goDCgo.com, was designated as a
secondary service.
Support services included services, such as general marketing and media outreach, which primarily inform
customers of travel options or other program services; in essence they offer a “referred” influence. They can
10. goDCgo – July 2014-June 2015 Program Impact Report
8
directly motivate mode change with no intermediate contact, but these impacts are difficult to measure. Unlike
services that require a registration, most information and outreach services do not record names of individual
users who can be contacted in a follow-up survey. These impacts are best measured through area-wide surveys
that assess commuters’ awareness of informational messages and define mode changes that were motivated by
the messages. Due to the lack of available data for this purpose, the evaluation does not attempt to quantify
independent impacts from marketing activities. Referred impacts are included, however, through use of the
referred services that they promote.
Factors Used in the Calculations
The evaluation method utilizes factors related to participation in each service and behavioral change resulting from
that participation. Three types of data serve as the basic factors for the impact measurement:
1) Level of participation in each service (population base)
2) Shifts to alternative modes as a result of the program (placement rate)
3) Average trip and VMT reductions from individual mode shifts (VTR factor and average travel distance)
Service Participation / Population Base – Table 3 presents the participation figures for each of the services directly
estimated in the calculation. These figures were obtained from goDCgo tracking data and other sources as
appropriate.
Table 3 – Program Participation in Individual Services in the 2014 Evaluation
Service Participation/Users
- Ridematch assistance (MWCOG database) 530 applicants
- Continued employer services (clients as of June 2015) 733 employer clients, 237,805 employees
- New / Expanded employer services (since June 2014)
New employer services 65 employer clients, 42,166 employees
Expanded employer services 6 employer clients, 5,050 employees
- Capital Bikeshare
21,255 annual members;
20,405 employed members
- Bike-to-Work Day event (NEW) 5,900 registrants at DC pit stops
- Residential services (clients as of June 2015) (NEW)
46 buildings reporting service level,
Estimated 10,542 residents
- DC Circulator (DC Circulator dashboard)
3,959,338 annual ridership Monday-Friday
Estimated 2,191,310 commute ridership
- goDCgo.com website 98,819 unique users
In defining the participation / population base, the impact calculation also considers that some participation
counts reflect multiple uses of a service by a single user. For example, a customer might use the goDCgo.com
website more than once in a year, to check schedules for various trips. Additionally, while services are available to
both employed and non-employed residents and to local and out-of-town users, the evaluation estimates only
impacts resulting from local commute to work trip use, so the evaluation discounts the participation to measure
behavior changes only for local employed users and for commute travel. Factors for employed percentage and
commute use were obtained from user survey data.
11. goDCgo – July 2014-June 2015 Program Impact Report
9
Impact Multiplier Factors –The impact calculation method applied a series of service-specific multiplier factors to
the participation counts to estimate impacts. Table 4 presents the key factors for each service included in the 2014
calculation: placement rates, influence factors, VTR factors, travel distances, and drive alone access. For example,
the continued placement rate (influenced) for the Rideshare assistance database is 33% and the continued vehicle
trip reduction (VTR) factor is 0.40 trips reduced per day.
When possible, the calculation methodology derives multiplier factors from data collected on service use and
mode changes, through follow-up contacts with goDCgo service users. If factors could not be derived directly, due
to lack of data specific to a service, the consultant used multiplier values derived for similar programs in the
Washington metropolitan region that have conducted individual service evaluations. Appendix 1-e documents the
sources of data for each factor.
The calculation approach first calculated
impacts for individual services as if they
were stand-alone services. But as noted
earlier, there is overlap among the
services. To correct for the overlap and
avoid double or triple counting
participating commuters, the consultants
derived discount factors to reflect the
estimated share of the service impact
that was independent of other services.
These discount factors were multiplied by
the trip, VMT, and emission impacts
calculated for each service individually to
reduce individual service impacts.
Appendix 1-c presents the overlap
adjustments.
The final step in the calculation was to add all the discounted impacts for each program together, to produce the
total aggregate impacts for all services combined. These impacts were presented in Table 1 above and are
summarized in Appendix 1-d.
12. goDCgo – July 2014-June 2015 Program Impact Report
1
Table 4 – Multiplier Factors by Service – 2014 Evaluation
Calculation Factor Ridematch
Continued
Employers
Expanded/
New
Employers
Capital
Bikeshare
Marketing
Bike-to-
Work Day
Marketing
Residential
Services
DC
Circulator
Marketing
goDCgo.com
Website
Placement rate (base)
- Continued 33% 19% 10% 44% 32% 1% 10% 10%
- Temporary 5% 0% 0% 0% 0% 0% 0% 10%
Influence Rate 75% 51% 72% 40% 19% 43% 25% 50%
VTR Factor
- Continued 0.40 1.2 1.2 0.16 0.24 0.8 1.0 0.3
- Temporary 0.18 --- --- --- --- --- --- 0.9
One-way Travel Distance
- Continued 29.0 15.5 15.5 5.7 7.5 8.3 5.0 15.6
- Temporary 26.0 --- --- --- --- --- --- 15.6
Drive alone Access Distance
- DA access percentage 21% 42% 42% 0% 0% 0% 0% 30%
- DA access distance 0.9 4.7 4.7 0.0 0.0 0.0 0.0 3.0
13. goDCgo – July 2014 – June 2015 Program Impact Report
2
Appendices
2014 Impact Calculation Worksheets
1-a Calculation Factors
1-b Vehicle Trip and VMT Impacts - Calculation by Service
1-c Service Overlap Factors
1-d Summary of Program Impacts
1-e Notes on Data Sources
14. Appendix 1-a
2014 Impact Calculation – Calculation Factors
Ridematch
Continued
Employers
Expanded/
New
Employers
Capital
Bikeshare
Bike-to-
Work Day
Residential
Services
DC
Circulator
goDCgo.com
Website
Participation base 530 237,805 47,166 21,255 5,900 10,543 3,959,338 98,819
- Total number of uses Applicants
Employees at
client site
Employees at
client site
Annual
members
DC
registrants
Residents at
client site
Annual M-F
ridership
Unique
users
Repeat use discount
- Local employed percentage 100% 100% 100% 96% 100% 90% 100% 43%
- Commute trip share 100% 100% 100% 100% 100% 100% 55% 100%
- Average annual use* 1 1 1 1 1 1 250 1
- Repeat adjustment 100% 100% 100% 100% 100% 100% 0.4% 100%
Customer base
- Number of unique users 530 237,805 47,166 20,405 5,900 9,488 8,711 42,690
Placement rate
- % users with continued mode chg 33% 19% 10% 440% 32% 1% 10% 10%
- % users with temporary mode chg 5% 0% 0% 0% 0% 0% 0% 10%
Influenced Placement rate
- Net influence rate 75% 51% 72% 40% 19% 43% 25% 50%
- Continued rate 25% 10% 7% 18% 6% 0.4% 3% 5%
- Temporary rate 4% 0% 0% 0% 0% 0% 0% 5%
VTR factor
- Continued VTR 0.40 1.20 1.20 0.16 0.24 0.80 1.00 0.30
- Temporary VTR 0.18 0.00 0.00 0.00 0.00 0.00 1.00 0.90
- Temporary duration (weeks) 7 0 0 0 0 0 0 4
- Temporary duration % 13% 0% 0% 0% 0% 0% 0% 8%
One Way distance
- Continued distance 29.0 15.5 15.5 5.7 7.5 8.3 5.0 15.6
- Temporary distance 26.0 15.5 15.5 0.0 7.5 8.3 5.0 15.6
Drive Alone access
- % changers who DA to alt mode 21% 42% 42% 0% 0% 0% 0% 30%
- DA distance to alt mode 0.9 4.7 4.7 0.0 0.0 0.0 0.0 3.0
16. Appendix 1-b
2014 Impact Calculation – Vehicle Trip and VMT Impacts - Calculation by Service
Ridematch
Continued
Employers
Expanded/
New
Employers
Capital
Bikeshare
Bike-to-
Work Day
Residential
Services
DC
Circulator
goDCgo.com
Website
Placements
- Continued Placements 130 23,043 3,510 3,591 354 40 218 2,134
- Temporary Placements 20 0 0 0 0 0 0 2,134
Vehicle Trips Reduced
- Continued Vehicle Trips 52 27,652 4,212 575 85 32 218 640
- Temporary Vehicle Trips 0 0 0 0 0 0 0 148
- Total Vehicle Trips Reduced 52 27,652 4,212 575 85 32 218 788
VMT Reduced
- Continued VMT 1,508 428,606 65,286 3,278 638 266 1,090 9,984
- Temporary VMT 0 0 0 0 0 0 0 2,309
- Total VMT Reduced 1,508 428,606 65,286 3,278 638 266 1,090 12,293
Drive Alone Access Adjustment
- DA access trips 11 11,614 1,769 0 0 0 0 236
- Net Vehicle trips reduced 41 16,038 2,443 575 85 32 218 552
- DA access VMT 10 54,586 8,314 0 0 0 0 708
- Net VMT reduced 1,498 374,020 56,972 3,278 638 266 1,090 11,585
Double Count Correction
- % credited to other service 25% 0% 0% 15% 50% 0% 25% 20%
- Net credit to service 75% 100% 100% 85% 50% 100% 75% 80%
Summary (adj. double count)
- Total placements 113 23,043 3,510 3,052 177 40 164 3,414
- Total Vehicle Trips Reduced 39 27,652 4,212 489 43 32 164 630
- Total VMT Trips Reduced 1,131 428,606 65,286 2,786 319 266 818 9,834
- Net VTrips - emission est 31 16,038 2,443 489 43 32 164 442
- Net VMT - emission est 1,124 374,020 56,972 2,786 319 266 818 9,268
17. Appendix 1-c
2014 Impact Calculation – Service Overlap Factors
Base Program NET Ridematch
Continued
Employers
Expanded/
New
Employers
Capital
Bikeshare
Bike-to-
Work Day
Residential
Services
DC
Circulator
goDCgo
Website
RideMatch 75% 25%
Continued employer 100%
Expanded / New employer 100%
Capital Bikeshare 85% 5% 5% 5%
Bike to Work Day (NEW) 50% 50%
Residential Services (NEW) 100%
DC Circulator 75% 10% 10% 5%
goDCgo.com website 80% 5% 10% 5%
18. Appendix 1-d
Summary of Program Impacts for 2014, 2013, 2012, 2011, and 2010 and Comparison of 2014 to 2013 Impacts
Impact Indicator
2014
Impacts
2013
Impacts
Change
2014 vs
2013
2012
Impacts
2011
Impacts
2010
Impacts
Total Placements 33,513 32,100 1,413 29,527 27,398 19,658
Travel Impacts (daily)
- Total Vehicle Trips Reduced 33,261 32,325 936 31,892 31,490 23,038
- Total VMT Reduced 509,046 481,044 28,002 494,827 489,134 357,386
Emission Impacts (daily kg)
- NOx 197 186 11 180 177 130
- VOC 97 93 4 102 100 73
- CO2 184,796 173,931 10,865 202,634 200,210 146,175
Energy Savings (daily)
- Gallons of gas saved 18,722 17,608 1,114 18,465 18,244 13,320
19. Appendix 1-e
2014 Impact Calculation – Notes on Data Sources
Participation Counts
- Ridematch requests - COG data (est 530 applications received between July 2014 and June 2015)
- Continued Employers (continued from June 2014 - no change) - 733 employers, 237,805 employees
- Expanded employers (included in June 2014, but expanded services) - 6 employers, 5,050 employees
- New employers - 65 employers, 42,116 employees
- Capital Bikeshare - 21,255 "annual" DC members x 96% employed (2012 CB survey) = 20,405
- Bike-to-Work Day event - 5,900 riders registered at DC pit stop
- Residential services - 46 buildings, Estimated 10,542 residents
- DC Circulator - M-F ridership June 2014-May 2015, with discounts by route for assumed commute use: GT-Union Sta (60%), Navy Yard (50%), Dupont Circle
(60%), Woodley/Adams Morgan (50%), Potomac Avenue (50%) - based on estimate from goDCgo staff - Total M-F ridership = 3,959,338; Commute ridership =
2,191,310
- goDCgo.com website - Number of unique users, discounted to include only local DC users (32%)
Placement Rates
- Ridematch requests - MWCOG data - 2014 placement survey (in MSA only)
- Continued Employers - Derived from EPA COMMUTER Model, using program characteristics for each employer
- New Employers - Derived from EPA COMMUTER Model, using actual program characteristics for each employer
- Capital Bikeshare - 2014 CB member survey - placement for commute trips only
- Bike-to-Work Day event - 2013 BTW Day survey (MWCOG) - 10.7% of participants started riding and 21.8% increased frequency of riding to work
- Residential services - Assumed 1% placement
- DC Circulator – Estimated from DC Circulator Rider Survey (fall 2012)
- GoDCgo.com website - estimate from Arlington County, ACCS, commuterpage.com survey
VTR Factor
- Ridematch requests - MWCOG data - 2014 placement survey (in MSA only)
- Continued Employers - Assumed to be 1.20; majority of shifts to transit
- New Employers - Assumed to be 1.20; majority of shifts to transit
- Capital Bikeshare - 2014 CB member survey - placement for commute trips only
- Bike-to-Work Day - 2013 BTW Day survey (MWCOG) - VTR factor calculated as annual average, accounting for lower use during winter months
- Residential services - Assumed 2 days per week shift from drive alone to transit/bike/walk
- DC Circulator – Estimated from DC Circulator Rider Survey (fall 2012)
- GoDCgo.com website - estimate from Arlington County, ACCS, commuterpage.com survey
20. Travel Distance
- Ridematch requests - MWCOG data - 2014 placement survey (in MSA only)
- Continued Employers - State of Commute 2013 survey - average for commuters traveling to DC worksite
- New Employers - State of Commute 2013 survey - average for commuters traveling to DC worksite
- Capital Bikeshare - 2014 CB member survey - commute distance for commute changers only
- Bike-to-Work Day event - 2013 BTW Day survey (MWCOG) - distance calculated for riders who started/increased riding to work
- Residential Services - State of Commute 2013 survey - average for commuters who live in DC
- DC Circulator – Estimated from DC Circulator Rider Survey (fall 2012)
- GoDCgo.com website - State of Commute 2013 survey - average for commuters traveling to DC worksites
Drive alone Access
- Ridematch requests - MWCOG data - 2013 SOC survey, DC residents
- Continued Employers - State of Commute 2013 survey - average for commuters traveling to DC worksite
- New Employers - State of Commute 2013 survey - average for commuters traveling to DC worksite
- Capital Bikeshare - assumed to be 0
- Bike-to-Work Day Event - assumed to be 0
- Residential services - assumed to be 0
- DC Circulator - assumed to be 0 - no P&R lots
- GoDCgo.com website - State of Commute 2013 survey - average for commuters traveling to DC worksites
Note on Employer Services
Employer services results will not match MWCOG 2014 TERM results for DC Employer Outreach; the TERM excludes a sub-set of client employers (Levels 1-2,
public agencies, employers that were clients prior to the TERM adoption)