Route optimization algorithm are the mathematical formula that solve routing problems..
Some types of routing:
1) Vehicle Routing Problem (VRP)
2) Traveling Salesman Problem (TSP)
3) Ant Colony Optimization (ACO)
The document summarizes an optimization program that airlines can use to determine the right freight capacity, operating frequency, and fleet positioning to minimize costs and maximize profits. The program takes in data on routes, yields, demands, and costs. It then runs integer programming models and U-curve techniques to find the optimum solution. A case study on Yemenia airline shows how the program can determine the best aircraft types for its network and maximize profits on a multi-stop route from Sana'a to Singapore.
A TMS is a part of Supply Chain Management specially designed for a broader goal to ease and automate transportation operation by allowing <a href="https://www.lotus-containers.com/de/">shipping companies</a> to gain valuable insight of every point of distribution to enhance efficiency and increases productivity.
The document discusses ant colony optimization (ACO), which is a metaheuristic algorithm inspired by the behavior of real ant colonies. It describes how real ants deposit pheromone trails to communicate indirectly and find the shortest path between their colony and food sources. The algorithm works by "artificial ants" probabilistically building solutions to optimization problems and adjusting pheromone levels based on solution quality, similar to how real ants reinforce shorter paths. It provides examples of how ACO has been applied to problems like the traveling salesman problem and discusses some extensions to the basic ACO algorithm.
Ant colony optimization is a metaheuristic algorithm that is inspired by the behavior of real ant colonies. Real ants deposit pheromone on paths between their nest and food sources, and other ants are more likely to follow paths with higher pheromone densities, allowing the colony to find the shortest path over time without centralized control. The algorithm models this behavior to solve optimization problems, with artificial ants probabilistically building solutions and adjusting pheromone levels to bias toward better solutions. The presentation discusses how ant colony optimization works and its components, including probabilistic solution construction, pheromone updating, and evaporation. It then provides an example application of using ant colony optimization for adaptive routing in communication networks.
This document discusses using fuzzy multi-objective linear programming (FMOLP) to solve the traveling salesman problem (TSP). TSP aims to find the shortest route to visit each city once. FMOLP handles fuzzy constraints and goals. It formulates TSP as three objectives - minimizing cost, distance, and time. A case study applies FMOLP to find the optimal route between 4 cities. The solution maximizes satisfaction level α while meeting fuzzy constraints on each objective.
The document discusses various formulations of the Vehicle Routing Problem with Backhauls (VRPB). It begins by providing background on the VRPB and its history. It then describes several common variants of the VRPB that have been studied in literature, including the Vehicle Routing Problem with Backhauls (VRPB), Mixed Vehicle Routing Problem with Backhauls (MVRPB), Multiple Depot Mixed Vehicle Routing Problem with Backhauls (MDMVRPB), Vehicle Routing Problem with Backhauls and Time Windows (VRPBTW), and others. For each variant, the document outlines key characteristics and constraints and references relevant literature and studies.
A fleet management system comprises optimally planning, supervising, and controlling fleet operations using available resources and information systems. It integrates organizational processes. Key applications include vehicle tracking, health/safety tracking, fuel/speed management, order transmission, route planning, and driver/vehicle management. Route planning algorithms like the traveling salesman problem, vehicle routing problem, and pickup and delivery problem are used to arrange transport orders into optimal vehicle tours. GPS tracking uses satellite signals to track vehicle locations in real-time, improving efficiency, reducing costs, and increasing transparency of transport activities.
The document summarizes an optimization program that airlines can use to determine the right freight capacity, operating frequency, and fleet positioning to minimize costs and maximize profits. The program takes in data on routes, yields, demands, and costs. It then runs integer programming models and U-curve techniques to find the optimum solution. A case study on Yemenia airline shows how the program can determine the best aircraft types for its network and maximize profits on a multi-stop route from Sana'a to Singapore.
A TMS is a part of Supply Chain Management specially designed for a broader goal to ease and automate transportation operation by allowing <a href="https://www.lotus-containers.com/de/">shipping companies</a> to gain valuable insight of every point of distribution to enhance efficiency and increases productivity.
The document discusses ant colony optimization (ACO), which is a metaheuristic algorithm inspired by the behavior of real ant colonies. It describes how real ants deposit pheromone trails to communicate indirectly and find the shortest path between their colony and food sources. The algorithm works by "artificial ants" probabilistically building solutions to optimization problems and adjusting pheromone levels based on solution quality, similar to how real ants reinforce shorter paths. It provides examples of how ACO has been applied to problems like the traveling salesman problem and discusses some extensions to the basic ACO algorithm.
Ant colony optimization is a metaheuristic algorithm that is inspired by the behavior of real ant colonies. Real ants deposit pheromone on paths between their nest and food sources, and other ants are more likely to follow paths with higher pheromone densities, allowing the colony to find the shortest path over time without centralized control. The algorithm models this behavior to solve optimization problems, with artificial ants probabilistically building solutions and adjusting pheromone levels to bias toward better solutions. The presentation discusses how ant colony optimization works and its components, including probabilistic solution construction, pheromone updating, and evaporation. It then provides an example application of using ant colony optimization for adaptive routing in communication networks.
This document discusses using fuzzy multi-objective linear programming (FMOLP) to solve the traveling salesman problem (TSP). TSP aims to find the shortest route to visit each city once. FMOLP handles fuzzy constraints and goals. It formulates TSP as three objectives - minimizing cost, distance, and time. A case study applies FMOLP to find the optimal route between 4 cities. The solution maximizes satisfaction level α while meeting fuzzy constraints on each objective.
The document discusses various formulations of the Vehicle Routing Problem with Backhauls (VRPB). It begins by providing background on the VRPB and its history. It then describes several common variants of the VRPB that have been studied in literature, including the Vehicle Routing Problem with Backhauls (VRPB), Mixed Vehicle Routing Problem with Backhauls (MVRPB), Multiple Depot Mixed Vehicle Routing Problem with Backhauls (MDMVRPB), Vehicle Routing Problem with Backhauls and Time Windows (VRPBTW), and others. For each variant, the document outlines key characteristics and constraints and references relevant literature and studies.
A fleet management system comprises optimally planning, supervising, and controlling fleet operations using available resources and information systems. It integrates organizational processes. Key applications include vehicle tracking, health/safety tracking, fuel/speed management, order transmission, route planning, and driver/vehicle management. Route planning algorithms like the traveling salesman problem, vehicle routing problem, and pickup and delivery problem are used to arrange transport orders into optimal vehicle tours. GPS tracking uses satellite signals to track vehicle locations in real-time, improving efficiency, reducing costs, and increasing transparency of transport activities.
This document discusses mode choice models and discrete choice modeling. It begins by introducing the concept of an individual choosing among different transportation modes for a specific trip. The individual's choice set includes all available modes. Mode choice models aim to understand this decision in terms of observable factors. The document then discusses how discrete choice modeling formulates the individual's decision as choosing exactly one alternative from their choice set. It also introduces the concepts of systematic and idiosyncratic components of utility to represent observable and unobservable factors influencing choice. The document concludes by discussing binary choice probabilities and how they relate to the probability that a given mode maximizes an individual's utility.
Research presentation on Autonomous Driving. Direction perception approach.
Research work by Princeton University group.
Note: Link given in the presentation
This document provides an overview of Intelligent Transport Systems (ITS) including a brief history, applications, and examples of ITS implementations in India. Some key points:
- ITS apply information technologies like sensors and computers to improve transport network operations, acquiring data on traffic and using it to guide traffic, enhance safety, and reduce costs.
- Common ITS applications include traffic monitoring, traveler information systems, vehicle control systems, and public transport management.
- Early ITS development began in the 1960s with systems in the US and Europe, with many countries now implementing ITS technologies and applications.
- India has piloted various ITS such as automatic traffic control systems in major cities, travel
The document discusses the travelling salesman problem (TSP) which aims to find the shortest route for a salesman to visit each city in a list only once and return to the original city. It provides examples of different paths through cities and notes there are (n-1)! possible solutions when there are n cities. The document outlines real-world applications of TSP and methods for solving it, including trying every possibility for small problems or using optimization methods for larger problems. It gives two examples of TSP problems and shows the solutions using an assignment algorithm and considering additional constraints to avoid sub-tours.
This document discusses vehicle routing and scheduling models and algorithms. It introduces basic models like the Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), and Pickup and Delivery Problem with Time Windows (PDPTW). Construction heuristics like savings, insertion, and set covering algorithms are presented to find initial feasible solutions that can then be improved using local search methods. The document outlines practical considerations and recent variants like dynamic and stochastic routing problems.
The document discusses the Travelling Salesman Problem (TSP), which aims to find the shortest route to visit each city in a list exactly once and return to the origin city. It describes TSP as an NP-hard problem, belonging to the complexity class NP-complete. The document provides background on TSP, explaining it cannot be solved in polynomial time using techniques like linear programming. While an efficient solution to the general TSP has not been found, there are approximation algorithms that provide near-optimal solutions.
The document discusses the traveling salesman problem (TSP) and how ant colony optimization (ACO) algorithms can be used to find optimal or near-optimal solutions. It provides an overview of ACO, including how artificial ants deposit and follow pheromone trails to probabilistically construct solutions. The ACO algorithm is described and an example TSP problem with 4 cities (A, B, C, D) is shown across 4 iterations to demonstrate the algorithm. Advantages are noted such as efficiency for small problems and ability to adapt to changes, while disadvantages include slow convergence time for large problems.
This document discusses vehicle detection using image processing. It describes how sensors can detect vehicles using transducers to detect their presence and convert the output into electrical signals. Sensors are either in-roadway, requiring installation in the road, or over-roadway, mounted above the road. The document focuses on detecting vehicles using image and video processing by extracting the vehicle portion from images in both the spatial and frequency domains, and matching the vehicle's aspect ratio to detect its type. It proposes applications for automated traffic management, toll collection, and security.
This document summarizes an academic presentation on Ant Colony Optimization given by Adrian Wilke on January 18, 2011. It discusses the history and experiments that inspired Ant Colony Optimization algorithms. It then describes the Ant System algorithm in detail, including how it uses a transition probability formula to select the next node, updates pheromone trails, and provides the full algorithm. Finally, it briefly discusses some results from applying the Ant System to optimization problems and compares it to the later AntNet algorithm.
Solving the traveling salesman problem by genetic algorithmAlex Bidanets
The document discusses the traveling salesman problem and genetic algorithms. The traveling salesman problem involves finding the shortest route to visit each city on a list only once and return to the origin city. Genetic algorithms provide a method to solve optimization problems like the traveling salesman problem. Genetic algorithms work by initializing a population of solutions and using operators like crossover and mutation to generate new populations, selecting the fittest solutions to reproduce until a condition is met. The genetic algorithm approach allows the traveling salesman problem to be solved effectively without prior knowledge of the problem.
The document discusses intelligent transportation systems (ITS) and their potential applications in India. It defines ITS as adding information and communication technologies to transportation infrastructure and vehicles to improve safety, reliability, efficiency, and quality of transportation. The document then outlines several ITS applications currently used worldwide like electronic toll collection and emergency notification systems. It discusses issues with transportation in India like high accident rates. Finally, it proposes areas where ITS could help in India like commercial vehicle management, emergency response, and improving public transportation.
Fleet management involves optimally planning, supervising, and controlling fleet operations using available resources while considering internal and external factors. It focuses on integrating organizational processes with modern information systems. Fleet management systems allow companies to track vehicles using GPS, monitor speed and idle time, plan routes, manage drivers, track assets, and generate various reports. They provide advantages such as improved productivity, reduced costs, enhanced safety, and more transparent transport operations.
This document summarizes a machine learning project for Homesite to predict customer quote conversions. The team members are Jack, Harry, and Abhishek. Homesite wants to predict the likelihood of customers purchasing insurance contracts based on their quote. The training data has 261k rows and 298 predictors, while the test data has 200k rows and the same 298 columns. Some key steps included data cleaning, using gradient boosting and random forests, and calculating the AUC (area under the ROC curve) metric to evaluate model performance. The team's model achieved an AUC of 0.95, indicating it does not overfit and has little bias.
Last-mile delivery is the final stage in the network of courier, express, and parcel companies
(CEP). It is an entire ecosystem that brings a variety of goods to consumers’ doorsteps (or
very close). In 2016, we looked at the transport market – and in particular last-mile delivery –
from two industry perspectives: commercial vehicles (advanced industries sector) and CEP
(logistics sector). Our analyses revealed three main insights
The document discusses Intelligent Transportation Systems (ITS), which use information and communication technologies to improve transportation outcomes such as safety, productivity, reliability and more. ITS technologies can include wireless communications, computational technologies, floating car data collection using cellular signals, and sensing technologies like inductive loops and video detection. The document outlines several ITS applications including emergency notification systems, automatic road enforcement, collision avoidance systems and more. Benefits of ITS include reduced accidents, time savings, lower emissions and costs.
Smart mobility uses information technology to improve transportation through more affordable and sustainable options. A smart mobility strategy organizes current and planned efforts under one umbrella to implement solutions to immediate problems and lay the groundwork for emerging technologies through an interdepartmental team. We offer smart mobility solutions like free public WiFi, traffic management systems, emergency vehicle preemption, smart gate parking, vehicle counting and license plate recognition, drone surveillance, transportation info apps, security systems, visitor management, and smart lighting.
Automatic vehicle location (AVL) systems track and report vehicle locations accurately to help supervise, control, and communicate with vehicles. This helps monitor vehicle status and prevent illegal activities or problems. AVL systems collect data, preprocess it, analyze it, and apply the results. They inform supervisors of vehicle locations and routes, detect accidents, and monitor driver behavior like speeding or taking long breaks. AVL is useful for managing large fleets in industries like shipping, banking, and oil, helping increase profitability. While expensive to install, AVL provides long-term benefits to businesses by keeping track of vehicle conditions and driver actions.
The document discusses Intelligent Transportation Systems (ITS). ITS uses information and communication technologies to improve transportation outcomes like safety, productivity, travel reliability and more. Key ITS technologies discussed include wireless communications, computational technologies, floating car data collection, inductive loop detection, and video vehicle detection. Example ITS applications mentioned are emergency vehicle notification, automatic road enforcement, variable speed limits, collision avoidance systems, and dynamic traffic light sequencing.
This document discusses data analytics and optimization methods for assessing a ride-sharing system. It includes:
1) Developing a constraint-based model for acceptable ride matches using inferred parameters from historical trip data to account for route times and flexibility.
2) Analyzing historical trip schedules to identify imbalances between drivers and passengers that could limit participation.
3) Proposing and evaluating allowing drivers flexibility to also be passengers, finding this could potentially reduce unmatched participants by up to 80%.
LO5: Simulation of transit signal priority strategies for brt operationsBRTCoE
This document outlines a study on simulating transit signal priority strategies to benefit bus rapid transit operations. The study aims to evaluate different conditional priority strategies through traffic simulation and determine how signal priority can best be implemented on BRT corridors. Case studies of the Silver Line route in Boston and routes in Minneapolis and Santiago will be used to test priority techniques like green extension, red truncation, and phase skipping. The impacts on bus and traffic performance will be analyzed.
This document discusses mode choice models and discrete choice modeling. It begins by introducing the concept of an individual choosing among different transportation modes for a specific trip. The individual's choice set includes all available modes. Mode choice models aim to understand this decision in terms of observable factors. The document then discusses how discrete choice modeling formulates the individual's decision as choosing exactly one alternative from their choice set. It also introduces the concepts of systematic and idiosyncratic components of utility to represent observable and unobservable factors influencing choice. The document concludes by discussing binary choice probabilities and how they relate to the probability that a given mode maximizes an individual's utility.
Research presentation on Autonomous Driving. Direction perception approach.
Research work by Princeton University group.
Note: Link given in the presentation
This document provides an overview of Intelligent Transport Systems (ITS) including a brief history, applications, and examples of ITS implementations in India. Some key points:
- ITS apply information technologies like sensors and computers to improve transport network operations, acquiring data on traffic and using it to guide traffic, enhance safety, and reduce costs.
- Common ITS applications include traffic monitoring, traveler information systems, vehicle control systems, and public transport management.
- Early ITS development began in the 1960s with systems in the US and Europe, with many countries now implementing ITS technologies and applications.
- India has piloted various ITS such as automatic traffic control systems in major cities, travel
The document discusses the travelling salesman problem (TSP) which aims to find the shortest route for a salesman to visit each city in a list only once and return to the original city. It provides examples of different paths through cities and notes there are (n-1)! possible solutions when there are n cities. The document outlines real-world applications of TSP and methods for solving it, including trying every possibility for small problems or using optimization methods for larger problems. It gives two examples of TSP problems and shows the solutions using an assignment algorithm and considering additional constraints to avoid sub-tours.
This document discusses vehicle routing and scheduling models and algorithms. It introduces basic models like the Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), and Pickup and Delivery Problem with Time Windows (PDPTW). Construction heuristics like savings, insertion, and set covering algorithms are presented to find initial feasible solutions that can then be improved using local search methods. The document outlines practical considerations and recent variants like dynamic and stochastic routing problems.
The document discusses the Travelling Salesman Problem (TSP), which aims to find the shortest route to visit each city in a list exactly once and return to the origin city. It describes TSP as an NP-hard problem, belonging to the complexity class NP-complete. The document provides background on TSP, explaining it cannot be solved in polynomial time using techniques like linear programming. While an efficient solution to the general TSP has not been found, there are approximation algorithms that provide near-optimal solutions.
The document discusses the traveling salesman problem (TSP) and how ant colony optimization (ACO) algorithms can be used to find optimal or near-optimal solutions. It provides an overview of ACO, including how artificial ants deposit and follow pheromone trails to probabilistically construct solutions. The ACO algorithm is described and an example TSP problem with 4 cities (A, B, C, D) is shown across 4 iterations to demonstrate the algorithm. Advantages are noted such as efficiency for small problems and ability to adapt to changes, while disadvantages include slow convergence time for large problems.
This document discusses vehicle detection using image processing. It describes how sensors can detect vehicles using transducers to detect their presence and convert the output into electrical signals. Sensors are either in-roadway, requiring installation in the road, or over-roadway, mounted above the road. The document focuses on detecting vehicles using image and video processing by extracting the vehicle portion from images in both the spatial and frequency domains, and matching the vehicle's aspect ratio to detect its type. It proposes applications for automated traffic management, toll collection, and security.
This document summarizes an academic presentation on Ant Colony Optimization given by Adrian Wilke on January 18, 2011. It discusses the history and experiments that inspired Ant Colony Optimization algorithms. It then describes the Ant System algorithm in detail, including how it uses a transition probability formula to select the next node, updates pheromone trails, and provides the full algorithm. Finally, it briefly discusses some results from applying the Ant System to optimization problems and compares it to the later AntNet algorithm.
Solving the traveling salesman problem by genetic algorithmAlex Bidanets
The document discusses the traveling salesman problem and genetic algorithms. The traveling salesman problem involves finding the shortest route to visit each city on a list only once and return to the origin city. Genetic algorithms provide a method to solve optimization problems like the traveling salesman problem. Genetic algorithms work by initializing a population of solutions and using operators like crossover and mutation to generate new populations, selecting the fittest solutions to reproduce until a condition is met. The genetic algorithm approach allows the traveling salesman problem to be solved effectively without prior knowledge of the problem.
The document discusses intelligent transportation systems (ITS) and their potential applications in India. It defines ITS as adding information and communication technologies to transportation infrastructure and vehicles to improve safety, reliability, efficiency, and quality of transportation. The document then outlines several ITS applications currently used worldwide like electronic toll collection and emergency notification systems. It discusses issues with transportation in India like high accident rates. Finally, it proposes areas where ITS could help in India like commercial vehicle management, emergency response, and improving public transportation.
Fleet management involves optimally planning, supervising, and controlling fleet operations using available resources while considering internal and external factors. It focuses on integrating organizational processes with modern information systems. Fleet management systems allow companies to track vehicles using GPS, monitor speed and idle time, plan routes, manage drivers, track assets, and generate various reports. They provide advantages such as improved productivity, reduced costs, enhanced safety, and more transparent transport operations.
This document summarizes a machine learning project for Homesite to predict customer quote conversions. The team members are Jack, Harry, and Abhishek. Homesite wants to predict the likelihood of customers purchasing insurance contracts based on their quote. The training data has 261k rows and 298 predictors, while the test data has 200k rows and the same 298 columns. Some key steps included data cleaning, using gradient boosting and random forests, and calculating the AUC (area under the ROC curve) metric to evaluate model performance. The team's model achieved an AUC of 0.95, indicating it does not overfit and has little bias.
Last-mile delivery is the final stage in the network of courier, express, and parcel companies
(CEP). It is an entire ecosystem that brings a variety of goods to consumers’ doorsteps (or
very close). In 2016, we looked at the transport market – and in particular last-mile delivery –
from two industry perspectives: commercial vehicles (advanced industries sector) and CEP
(logistics sector). Our analyses revealed three main insights
The document discusses Intelligent Transportation Systems (ITS), which use information and communication technologies to improve transportation outcomes such as safety, productivity, reliability and more. ITS technologies can include wireless communications, computational technologies, floating car data collection using cellular signals, and sensing technologies like inductive loops and video detection. The document outlines several ITS applications including emergency notification systems, automatic road enforcement, collision avoidance systems and more. Benefits of ITS include reduced accidents, time savings, lower emissions and costs.
Smart mobility uses information technology to improve transportation through more affordable and sustainable options. A smart mobility strategy organizes current and planned efforts under one umbrella to implement solutions to immediate problems and lay the groundwork for emerging technologies through an interdepartmental team. We offer smart mobility solutions like free public WiFi, traffic management systems, emergency vehicle preemption, smart gate parking, vehicle counting and license plate recognition, drone surveillance, transportation info apps, security systems, visitor management, and smart lighting.
Automatic vehicle location (AVL) systems track and report vehicle locations accurately to help supervise, control, and communicate with vehicles. This helps monitor vehicle status and prevent illegal activities or problems. AVL systems collect data, preprocess it, analyze it, and apply the results. They inform supervisors of vehicle locations and routes, detect accidents, and monitor driver behavior like speeding or taking long breaks. AVL is useful for managing large fleets in industries like shipping, banking, and oil, helping increase profitability. While expensive to install, AVL provides long-term benefits to businesses by keeping track of vehicle conditions and driver actions.
The document discusses Intelligent Transportation Systems (ITS). ITS uses information and communication technologies to improve transportation outcomes like safety, productivity, travel reliability and more. Key ITS technologies discussed include wireless communications, computational technologies, floating car data collection, inductive loop detection, and video vehicle detection. Example ITS applications mentioned are emergency vehicle notification, automatic road enforcement, variable speed limits, collision avoidance systems, and dynamic traffic light sequencing.
This document discusses data analytics and optimization methods for assessing a ride-sharing system. It includes:
1) Developing a constraint-based model for acceptable ride matches using inferred parameters from historical trip data to account for route times and flexibility.
2) Analyzing historical trip schedules to identify imbalances between drivers and passengers that could limit participation.
3) Proposing and evaluating allowing drivers flexibility to also be passengers, finding this could potentially reduce unmatched participants by up to 80%.
LO5: Simulation of transit signal priority strategies for brt operationsBRTCoE
This document outlines a study on simulating transit signal priority strategies to benefit bus rapid transit operations. The study aims to evaluate different conditional priority strategies through traffic simulation and determine how signal priority can best be implemented on BRT corridors. Case studies of the Silver Line route in Boston and routes in Minneapolis and Santiago will be used to test priority techniques like green extension, red truncation, and phase skipping. The impacts on bus and traffic performance will be analyzed.
A Dynamic Vehicular Traffic Control Using Ant Colony And Traffic Light Optimi...Kristen Carter
This document proposes a dynamic vehicular traffic control system using ant colony optimization and optimized traffic lights. It aims to reduce traffic congestion in urban areas. The system divides the road network into cells and uses artificial ants to guide vehicles along the least congested paths within each cell. It also proposes a new method for optimizing traffic light timing at intersections based on real-time vehicle count data collected from vehicles and traffic lights using VANET technology. Simulation results using the DIVERT simulator show that the proposed traffic light optimization method improves average vehicle speed and reduces waiting times and stopped vehicles at intersections compared to a system with usual fixed-duration traffic lights.
This document discusses transportation corridor planning and analysis. It defines key terms like corridor, segment, point, and describes steps for corridor identification and analysis. Corridor analysis estimates performance by calculating capacity, travel time, and queue delay. Screen line and cordon line surveys are discussed to understand travel patterns and verify traffic models. In conclusion, congestion delay accounts for 28.9% of travel time and some sections show operational failure though vehicles pass without stopping.
Analysis of Traffic Behavior at the Toll Plazas Around BangaloreIRJET Journal
This document analyzes traffic behavior at three toll plazas in Bangalore, India. Traffic volume counts were conducted at the toll plazas to identify peak traffic hours. Statistical analysis using descriptive statistics was performed on the service time delay data collected for different vehicle types (cars, buses, trucks, etc.). The analysis found that truck traffic consumes more service time than other vehicle categories. Based on the study findings, the authors recommend providing separate toll lanes for different vehicle types to reduce delays.
A Review on Intrusion Detection System Based Data Mining TechniquesIRJET Journal
This document analyzes traffic behavior at three toll plazas in Bangalore, India. Traffic volume counts were conducted at the toll plazas to identify peak traffic hours. Statistical analysis using descriptive statistics was performed on the service time delay data collected for different vehicle types (cars, buses, trucks, etc.). The analysis found that truck traffic consumes more service time than other vehicle categories. Based on the study findings, the authors recommend providing separate toll lanes for different vehicle types to reduce delays.
The document proposes a cognitive urban transport system using autonomous electric buses and optimized routes determined by machine learning algorithms. Real-time passenger requests would be used to optimize bus routes to minimize travel time and congestion while maximizing passengers transported. Routes and bus assignments would be determined by metaheuristics algorithms and further refined by neural networks in real-time. The system aims to reduce individual car usage and the associated problems of congestion, pollution, and wasted time compared to traditional fixed public transport routes. Key challenges include integrating this dynamic system with other transport and ensuring reliable arrival times.
T drive enhancing driving directions with taxi drivers’ intelligenceJPINFOTECH JAYAPRAKASH
This paper presents a smart driving direction system that leverages taxi driver intelligence. It models traffic patterns and driver routes using a time-dependent landmark graph constructed from taxi trajectory data. It then designs a two-stage routing algorithm to provide users with the fastest route based on departure time, starting point, and destination. Evaluation shows the system's routes are 60-70% faster than competitors and 50% are at least 20% faster on average.
A New Paradigm in User Equilibrium-Application in Managed Lane PricingCSCJournals
Ineffective use of the High-Occupancy-Vehicle (HOV) lanes has the potential to decrease the overall roadway throughput during peak periods. Excess capacity in HOV lanes during peak periods can be made available to other types of vehicles, including single occupancy vehicles (SOV) for a price (toll). Such dual use lanes are known as “Managed Lanes.” The main purpose of this research is to propose a new paradigm in user equilibrium to predict the travel demand for determining the optimal fare policy for managed lane facilities. Depending on their value of time, motorists may choose to travel on Managed Lanes (ML) or General Purpose Lanes (GPL). In this study, the features in the software called Toll Pricing Modeler version 4.3 (TPM-4.3) are described. TPM-4.3 is developed based on this new user equilibrium concept and utilizes it to examine various operating scenarios. The software has two built-in operating objective options: 1) what would the ML operating speed be for a specified SOV toll, or 2) what should the SOV toll be for a desired minimum ML operating speed. A number of pricing policy scenarios are developed and examined on the proposed managed lane segment on Interstate 30 (I-30) in Grand Prairie, Texas. The software provides quantitative estimates of various factors including toll revenue, emissions and system performance such as person movement and traffic speed on managed and general purpose lanes. Overall, among the scenarios examined, higher toll rates tend to generate higher toll revenues, reduce overall CO and NOx emissions, and shift demand to general purpose lanes. On the other hand, HOV preferential treatments at any given toll level tend to reduce toll revenue, have no impact on or reduce system performance on managed lanes, and increase CO and NOx emissions.
This document describes a study that used simulation to evaluate different parking guidance policies for a connected vehicle intelligent parking system. The simulator modeled a parking lot with sensor-equipped and non-sensor vehicles. Four policies were tested: random assignment, nearest parking, maximum satisfaction guidance, and near-optimal guidance. The near-optimal policy aimed to maximize the quality of parking space information gathered by routing sensor vehicles to the most informative spaces. Simulation results found the near-optimal policy produced the most stable and accurate estimates of parking space occupancy over time, even with low numbers of sensor vehicles, making it the best policy for initial deployment of connected vehicle parking systems.
(Paper) Parking Navigation for Alleviating Congestion in Multilevel Parking F...Naoki Shibata
Kenmotsu, M., Sun, W., Shibata, N., Yasumoto, K. and Ito, M. : "Parking Navigation for Alleviating Congestion in Multilevel Parking Facility," Proc. of 2012 IEEE 76th Vehicular Technology Conference (VTC2012-Fall), Sep.2012.
Abstract - Finding a vacant parking space in a large crowded parking facility takes long time. In this paper, we propose a navigation method that minimizes the parking time based on collected real-time positional information of cars. In the proposed method, a central server in the parking facility collects the information and estimates the occupancy of each parking zone. Then, the server broadcasts the occupancy data to the cars in the parking facility. Each car then computes a parking route with the shortest expected parking waiting time and shows it to the driver. We conducted simulation-based evaluations of the proposed method using a realistic model based on trace data taken from a real parking facility. We confirmed that the proposed method reduced parking waiting time by 20%–70% even with low system penetration.
The document proposes a dynamic approach to optimize taxi sharing. It involves a dual-side taxi searching algorithm to retrieve taxis that can satisfy a user request while minimizing distance. A scheduling algorithm then determines the best taxi. The system architecture partitions the road network into grids and uses timestamps to determine taxi locations and routes. The approach aims to enhance taxi delivery capabilities and satisfy more passenger commute needs while reducing costs and increasing driver profits compared to individual rides.
Operatioal analysis of any road is necessary for its design,planning and implementation procedure.This article mostly deals with preliminary proposal of two lane road of eastern region of Nepal.due to increased traffic condition servicabilty and level of service of koshi higway is found to be very poor hence Dharan submetropolitan city's part is analysed.
This document discusses a study conducted in Ghana to develop passenger car equivalents (PCEs) for vehicles at signalized intersections within the Kumasi Metropolis. Data on discharge headways was collected at 11 intersections, 7 of which had roadside facilities that interfered with traffic flow. PCE values were calculated using the headway ratio method and found to be higher at intersections with roadside friction. The locally derived PCE values were larger than those adopted from other standards, better reflecting the impact of local traffic conditions on intersection performance. This highlights the need to develop PCEs specific to local roadway environments rather than adopting foreign values.
Replacing Manhattan Subway Service with On-demand transportationChristian Moscardi
1) The document proposes replacing subway service in Manhattan with on-demand ridesharing during overnight repair periods to reduce costs.
2) A simulation would be used to model routing on-demand vehicles to service subway trips between 12AM-5AM using demand data and routing algorithms.
3) Key metrics like vehicle needs, passenger wait times, and repair costs vs transportation costs would be compared to evaluate the alternative. The simulation aims to answer if on-demand ridesharing can adequately replace subway service during repairs.
Seeking to quantify the viability of operating microtransit shuttles from station to station during nighttime (and weekend) subway closures that the MTA will take to support their signal upgrades.
Optimization Approach for Capacitated Vehicle Routing Problem Using Genetic A...ijsrd.com
Vehicle Routing Problem (VRP) is a combinatorial optimization problem which deals with fleet of vehicles to serve n number of customers from a central depot. Each customer has a certain demand that must be satisfied using each vehicle that has the same capacity (homogeneous fleet). Each customer is served by a particular vehicle in such a way that the same customer is not served by another vehicle. In this paper, Genetic Algorithm (GA) is used to get the optimized vehicle route with minimum distance for Capacitated Vehicle Routing Problem (CVRP). The outcomes of GA achieve better optimization and gives good performance. Further, GA is enhanced to minimize the number of vehicles.
JAVA 2013 IEEE DATAMINING PROJECT T drive enhancing driving directions with t...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
T drive enhancing driving directions with taxi drivers’ intelligenceIEEEFINALYEARPROJECTS
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Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
5. Some Types of Routing
Vehicle Routing Problem (VRP)
Travelling Salesman Problem (TSP)
Ant Colony Optimization (ACO)
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6. INTRODUCTION
Routing optimization Algorithms basically designs for
the best routes to reduce travel cost, energy
consumption and time. Due to non-deterministic
polynomial-time hard complexity, many route
optimizations involved in real-world applications
require too much computing effort. Shortening
computing time for Routing optimization is a great
challenge for state-of-the-art local optimization
algorithms.
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7. Logistic Model
The applications of vehicle routing problem (VRP) are
very common in real life. It can be described by the
scenario that follows. Let consider a depot having a
fleet of vehicles with limited capacities and a set of
customers, each with a certain demand for the
merchandise or goods to be dispatched. The problem
is to determine optimal routings for each vehicle to
visit every customer exactly once in order to fulfill the
demand. The most common goal for optimization is to
minimize the overall distance travelled by the vehicles.
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8. Logistic Model
The vehicle routing problem has been one of the
elementary problems in logistics ever since because of its
wide use. Vehicle Routing Problem (VRP) can be described
as follows. Suppose there are M vehicles each of which has
a capacity of Q and N customers who must be served from
a certain depot(terminal station). The goods each customer
asks for and the distance between them are known in
advance. The vehicles start from the depot(terminal
station), supply the customers and go back to the depot. It
is required that the route of the vehicles should be
arranged appropriately so that the least number of vehicles
is used and the shortest distance is covered.
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9. Conditions
The following conditions must be satisfied:
The total demand of any vehicle route must not exceed
the capacity of the vehicle.
Any given customer is served by one, and only one
vehicle.
Customer delivery should be done efficiently and
economically.
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11. Proposed Methodologies
The methodologies used to determine the best vehicle
routing for truck dispatch system (TDS) are
Permutation Enumerator
Greedy Search Algorithm
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12. Permutation Enumerator
Initially the distance of the stations are considered as
known factors along with the capacity of the vehicles
used. Each vehicle is assigned to a set of stations based
upon the demand and capacity of the vehicles. First by
means of permutations and combinations possible set
of routes for each vehicles are formed. Among the
route combinations best routes are formed based upon
the distance i.e. based on shortest distances. This
method is suitable for least no of stations (n< 5).
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13. Greedy Search Algorithm
A “greedy algorithm” firstly, based on the list of nodes that
a truck is assigned to service, it starts the sequence by
choosing from the list a station that is nearest to the
terminal station. Then the next station in the sequence is
determined by choosing the station that is nearest to the
preceding station from the list of remaining stations. This
process is repeated, until all the stations have been
exhausted to form the complete sequence starting and
ending at the terminal station by knowing the distances to
be travelled by the vehicles using genetic algorithm an
optimized routing plan is formed for each set of vehicles.
This will help to reach the customers in both effective and
efficient manner.
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15. Shortest Route Calculations for
the Stations
For vehicle routing of truck dispatch system, finding a
shortest route.
8 stations including depot and no of vehicles used is
independent.
Condition chosen is 3 stations can be visited by a
vehicle at a time.
The no of stations and the no of visits by a vehicle can
be altered according to the conditions.
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16. Shortest Route Calculations for
the Stations
First by means of permutations the total no of
combinations for shortest path is found.
Permutation Formula:
nCr = n!/r! (n-r)!
The no of all combinations of ‘n’ things, taken ‘r’ at a time
By combination
The total no of stations = 7
No of vehicle = 3
Hence, by formula
nCr = 7C3= 35 combinations
The total no of stations and stations that a vehicle can visit
can be altered according to situation.
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18. Advantages :
Improved Methodology of Additional terminals
generation :
Stop of which service capacity in rush hours falls
between standard and can be taken as potential
terminal. In other words, a terrific signal stop system
can be potential terminal if its number of vehicles in
rush hours fall between 100 and 400.
Competition caused by Parallel routes and rail
route
It considers the number of shared stops or
overlapping length between bus routes and rail routes.
However, in real situations, competition caused by the
parallel routes is inevitable.
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19. Disadvantages:
More detailed flow analysis should be carried out at stop
level.
At the present, passenger flow analysis is mainly implemented at
the route level. If the passenger attraction can be dis-aggregated
to each top on the route so analysis will become un-accurate.
All the Optimized vehicles routes should be evaluated and
compared.
Due to limitation of research time, evaluation after
optimization is only carried out in some important areas such as
commercial and residential zones at the individual route level.
So the distributed optimization for vehicles along with flow
analysis of passengers is not particularly considered.
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21. Conclusions and Future Work
Vehicle routing is first initiated with number of
stations to be served and total no of vehicles employed
to serve the stations based upon permutations and
combinations. Based on permutation and
combinations routings were formed. In case of large
number of vehicles greedy search method is used to
find the distances between the stations and the vehicle
routes distances. Here vehicle routing has been done
based upon known demand and capacity of the
suppliers.
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