Microsimulation Model Design in Lower Manhattan: A Street Management Approach Varanesh Singh Arup 155 Avenue of the Americas, New York, NY 10013 212-896-3115 Varanesh.Singh@arup.com S. Brian Huey Arup 155 Avenue of the Americas, New York, NY 10013 212-896-3196 Brian.Huey@arup.com Trent Lethco Arup 155 Avenue of the Americas, New York, NY 10013 212-896-3265 Trent.Lethco@arup.com Peter Dunn Arup Level 17 1 Nicholson St, Melbourne Vic 3000 3-9668-5452 Peter.Dunn@arup.com.au Suchi Sanagavarapu New York City Department of Transportation 40 Worth St., Room 1012 New York, NY 10013 212-788-2128 email@example.com Submitted for Presentation and Publication 88th Annual Meeting Transportation Research Board Submitted July 31st, 2008 WORD COUNT: 4,310 Words + 3 Figures + 5 Tables = 6,310 Total
Singh, Huey, Lethco, Dunn, Sangavarapu 1ABSTRACT Microsimulation models are an invaluable tool for transportation professionals who evaluate andanalyze network-level transportation impacts. Modeling dense urban street networks like central businessdistricts present significant challenges due to their size, density and complexity. In 2004, the LowerManhattan Development Corporation (LMDC) funded the New York City Economic DevelopmentCorporation (NYCEDC) and the New York City Department of Transportation (NYCDOT) to contractArup to develop a microsimulation model of Lower Manhattan. This paper describes the design,calibration and validation procedures of a Q-Paramics microsimulation traffic model of Lower Manhattanin New York City. Lower Manhattan is the fourth largest central business district in the United States andone of the oldest and densest areas in New York City. It contains some of the highest levels of pedestrian,transit and automobile activity in America. As a result, the modeling process must account for a variety ofcomplex urban issues that are atypical in most microsimulation models. An extensive, multi-modal data collection effort was conducted to create a detailed set of data,which was then applied to the model design process. A street management framework was used to guidethe development of the network and address issues of vehicle assignment and route choice. The modelalso addressed issues associated with vehicle interactions in high pedestrian flows intersections,disparities in driver types, taxi maneuvers, delivery vehicles and other activities unique to central businessdistricts.
Singh, Huey, Lethco, Dunn, Sangavarapu 2ACKNOWLEDGEMENTS This project is made possible by a grant from the Lower Manhattan Development Corporation,which is funded through Community Development Block Grants from the U.S. Department of Housingand Urban Development. The authors would like to thank the following firms and individuals who have provided extensivesupport to the overall project. These include: Andrew Salkin, Joshua Kraus, Josh Rosenbloom, LuisSanchez, Meghann Rowley, Steven Weber (NYCDOT); Phil Plotch (LMDC); Venetia Lannon, JoanMcDonald, Michael Taylor (NYCEDC); Ken Hausman and Matt Jukes (StumpHausman); UmeshAvadhani (B-A Engineering). We would like to thank our current and former colleagues who have beenextensively involved in the project: Andrew Wisdom, Daniel Peterson, Jonathan Drescher and TimBryant.
Singh, Huey, Lethco, Dunn, Sangavarapu 3INTRODUCTION Microsimulation models are an invaluable tool for transportation professionals who evaluate andanalyze network-level transportation impacts. Modeling dense urban street networks like central businessdistricts present significant challenges due to their size, density and complexity. Effective management ofthe transportation network is a critical element in the redevelopment and long-term viability of NewYork’s Lower Manhattan Central Business District (FIGURE 1). As part of an effort to develop tools thatwill allow the City to assess the transport impacts of development, street closures and changes to the roadnetwork, the Lower Manhattan Development Corporation (LMDC) funded the New York City EconomicDevelopment Corporation (NYCEDC) and the New York City Department of Transportation (NYCDOT)to contract Arup to undertake a multi-year effort to develop a microsimulation model of Lower Manhattanusing Quadstone’s Paramics (Q-Paramics) microsimulation software. Lower Manhattan is the fourth largest central business district in the United States, behindMidtown Manhattan, Chicago and Washington D.C. It is New York’s fastest growing residentialneighborhood, seeing a 145% increase in residential population since 2001. Lower Manhattan had 8.1million visitors in 2003 compared to 8.5 million in Midtown Manhattan and 1.1 million in Chicago(1,2,3) From a modeling perspective, Lower Manhattan presents a number of significant challenges. Thesize of the network means that the number of potential route choices for any trip is high. The level ofdemand and small block sizes mean that congestion develops quickly, making the model operation moresensitive to small changes in demand. Compounding all of this is the need to model interactions betweenvehicles and pedestrians, livery vehicles and goods delivery operations. As a result, the development ofthe Lower Manhattan simulation model addressed a variety of urban issues that typically don’t exist infreeway or corridor models. This paper focuses on the practical solutions that were developed in order to achieve a validatedmodel. It begins with the multimodal data collection and literature review process. Network design issuesare presented, specifically focusing on issues germane to urban modeling. Lastly, validation criteria andresults are presented and commented on.Previous Studies While there are no standardized guidelines for microsimulation modeling in New York City, therehave been several recently produced guidelines for the design and calibration of microsimulation modelsin America, Australia and the U.K. (4,5,6). These documents provide general guidance concerningscoping, data collection, base development, error checking and calibration. They do not provide manyspecific recommendations on issues pertaining to urban environments. Dowling and Skabardonis showthat a practical, top-down approach to the calibration stage can produce well calibrated models. Thisapproach was taken, with specific phases of the approach being elaborated on in this paper. However, theprocess is also general, and does not address software specific issues with Paramics (7). Severaldocuments provide specific calibration and validation criteria, which informed the calibration andvalidation criteria developed in this study (8 9,10,11).DATA COLLECTION An extensive data collection effort was conducted between 2003-2007, attempting to captureseasonal differences, multiple modes, parking and curbside activity.Counts The major component of the data collection effort was the turning movement counts. Counts wereconducted at approximately eighty key intersections within the study area during the fall of 2006. Thecounts were recorded in 15-minute intervals between the hours of 7:00-9:00 AM and 4:00-6:00 PM. The
Singh, Huey, Lethco, Dunn, Sangavarapu 4counts were classified based on vehicle types. In addition, automatic traffic recorder counts wereconducted on highways where human observation was not possible.Pedestrians Pedestrian counts were collected for 22 intersections within the study area in order to capturevehicle delay resulting from high pedestrian movements. Pedestrians were counted by direction at eachcrosswalk for during the hours of 6:00-10:00 AM and 3:00-7:00 PM on a Tuesday, Wednesday orThursday in early November, 2006.Parking Off-street parking surveys were taken in various parking lots throughout the study area to gain abetter understanding of the temporal flows into and out of parking lots during the AM and PM peak hour.A better understanding of parking lot flows was essential because they represent a major source/end oftrips within the internal study area. The counts were recorded in 15-minute intervals between the hours of 6:00-10:00 AM and 3:00-7:00 PM on a Tuesday, Wednesday or Thursday in January, 2007. The counts classified privateautomobiles, for hire vehicles and commercial vehicles.Livery Vehicles The primary source of taxi demand information was traffic surveys. While traffic surveys providean indication of the level of taxi activity, no information was available regarding travel characteristicsthrough the network. A future goal is to collect more detailed information about taxi routes and activitywith the cooperation of the taxi industry.Travel Time Surveys Travel time surveys were taken along ten different routes within the study area. These routes wereselected to allow for comparison of observed and modeled travel times along specific corridors or districtsduring the validation stage. The travel time surveys were conducted using a floating car technique where a two-person teamof surveyors drove the routes at the prevailing speed of traffic while recording the elapsed time betweenpre-determined control points such as the center of an intersection. The surveyors would also record thereason and length of time for each stoppage along the route. The reasons for stoppage includedcongestion, signal delay, curbside activity, incident or construction. Travel time surveys were conducted between 7:00-9:00 AM and 4:00-6:00 PM on a Tuesday,Wednesday or Thursday in November, 2006. The number of test runs captured in each survey session wasdependent on the route length and traffic conditions. On average, three to four runs were captured foreach travel time route for each time period.Curbside Parking Sample on-street surveys were used to understand curbside activity (double parking, picking up,and dropping off) on typical street blocks. These surveys were performed during peak hours and took intoaccount the type of vehicle that stopped along the curb, the arrival time and the departure time.Other Data The above data was augmented by a series of site visits to assess actual vehicle behavior andoperation. These qualitative assessments helped inform the visual audits of the model network. Othertraffic data made available by various authorities was utilized including BPM model and census datasets.
Singh, Huey, Lethco, Dunn, Sangavarapu 5NETWORK DESIGN Network design began with building the set of links and nodes in order to depict the physicalstreets of Lower Manhattan. Once this was done, vehicles, and roadways were configured to ensure thatvehicle behavior reflected the observed data.Vehicle types Fifteen vehicle types were specified in the Lower Manhattan model. Each vehicle type has uniquecharacteristics including physical dimensions, performance parameters, driver behavior parameters anddemand characteristics that affect performance. TABLE 1 describes the vehicle types, their parametersand typical route choice characteristics. The perturbation factor provides variability in route choice byadding a stochastic element to the generalized cost (described further in the route assignment section) ofeach possible route. The familiarity factor, expressed as a percentage, represents the proportion of driversassumed to have knowledge of the network. Familiar drivers makes a route choice based on minimizingtheir generalized cost regardless of link type, while unfamiliar drivers minimize their generalized cost, butare constrained to routes that are predominately over major road links. Light goods vehicles (deliveryvans) were given the same perturbation and familiarity factors as private cars because they were found toexhibit similar behavior compared to large trucks. The study area is unique because the density and frequency of bus services and the presence ofmany different operators. Bus routes and stops were coded based on public timetables, route maps andfield visits. Bus routes were designed to run beyond their route termination point in order to representrealistic conditions. Rather than buses disappearing from the network at the end of their route, bus routeswere coded to simulate deadheading to an appropriate exit point (like a layover area) in order to capturethe impact on other intersections. The data used in coding the bus routes was gathered from various sources such as published busschedules, studies (12) and discussions with New York City Transit. Because there are many private busoperators that have scheduled routes and stops in Lower Manhattan (coach and tour bus companies), notall bus data was available from the aforementioned sources. When data was not available assumptionswere made based on local knowledge of bus depots and layover areas.Road Hierarchy Aside from coding the physical roadway, traffic behavioral and operational characteristics mustbe taken into consideration. Adjacent land uses, traffic composition, pedestrians and transit activity allimpact traffic operations in Lower Manhattan. While many of these impacts cannot be explicitly modeledin the software, there are a series of parameters that can be applied to reflect these impacts. Therefore itwas important to understand and define the functional road hierarchy so that parameters can be applied
Singh, Huey, Lethco, Dunn, Sangavarapu 6consistently across the network. FIGURE 2 depicts a road hierarchy developed in a previous study of Lower Manhattan streets(10). In that study, the following street hierarchy is defined: • Through streets – Major traffic and bus movements through the area (ex. FDR, Route 9A). • Access streets – Major traffic and bus movements circulating within the area (ex. Broadway,Church Street). • Activity streets – Streets where land use consists of concentrations of retail and restaurants(ex. South Street, Chambers Street). • Support streets – Small streets serving delivery and pick-up, loading, entry to parking lots andsimilar activities (ex. Albany Street and Pearl Street).
Singh, Huey, Lethco, Dunn, Sangavarapu 7 • Residential streets – Streets where land use is primarily housing. This framework was shown to be a useful way to categorize streets in a systematic way, avoidingad hoc modifications to the link categories in the network. The framework was also advantageous in thatit considered important transportation characteristics beyond levels of traffic, such as land use, userperception and urban form. The number of link categories in the model was expanded to account for specific geometries andeffects from the major highways (FDR, Brooklyn Bridge, Route 9A) as well as narrow streets and alleys.TABLE 2 shows the definition of key groups of categories based on the hierarchy defined in FIGURE 3.Lane widths and speeds were input based on existing data, while category cost factors were based on acombination of the street framework, site knowledge and observation.Curbside Activity Curbside activity such as on street parking, livery pick up/drop off, goods delivery andconstruction delivery frequently occurs on streets in Lower Manhattan. This activity is typically midblockand creates small impediments to traffic flow in the network that can cumulatively create larger impacts.A number of approaches were considered. A multi-stage plug-in, developed by a third-party, offered thecapability to model curbside activity but presented upgrade and usability issues in this case. Alsoconsidered was placing zones on top of links, but this created problems with getting accurate linkmeasurements since vehicles would exit the network mid-link. It was determined that the most appropriate solution was to develop an on-street zone system, andlocate them perpendicular to links, throughout the network as destinations for taxi and goods deliveryvehicles. There were 78 zones representing on-street parking, livery vehicles and commercial loading andunloading. In addition there were 12 special zones representing security areas, loading docks andconstruction sites.Pedestrians Based on data collection and field observation, there is a high level of pedestrian and vehicleinteraction in Lower Manhattan. Studies of pedestrian level of service in Lower Manhattan havemeasured pedestrian volumes as high as 5,900 persons per hour in the AM period (14). Pedestriansimpact vehicular flow and vice versa, causing noticeable impacts on the network. Pedestrian movementhad to be represented in order to create an accurate model of Lower Manhattan. At the time of modeldevelopment, Paramics lacked the capability to explicitly model pedestrian movements in a network. As a result, the modeling team applied “dummy” signal phases to represent the delay to turningvehicles resulting from pedestrian movements. The dummy phase stopped traffic movements for aspecified period of time to account for conflicting pedestrian movement. The length of the phase wasbased on the overall length of the master phase, pedestrian occupancy and volume. This method wasbased on a standard method of calculating the percentage of time that pedestrians and vehicles are indirect conflict (15) and determining the delay in excess of the programmed pedestrian signal phases ateach intersection. Because right-turn-on-red movements are not allowed on New York City streets, themovement is prohibited in the model, and therefore interactions between vehicles and pedestrians areassumed to only occur when vehicles are making right or left turns on green. Shorter phases were shown to result in shorter dummy phases and longer phases resulted inlonger dummy phases. The approach adopted the following principles: • Dummy phases were not applied in instances where there was an all pedestrian phase; • Dummy phases were not applied to movements where pedestrians were prohibited fromcrossing; • If there was a leading pedestrian interval, the length of the interval was deducted from thedummy phase due to pedestrians being allowed to clear the conflict zone prior to the start of the turningvehicular phase.
Singh, Huey, Lethco, Dunn, Sangavarapu 8 It is not possible to model vehicle and pedestrian conflicts in the same manner at unsignalizedintersection. In addition, unsignalized intersections typically have low volumes of vehicles andpedestrians. Therefore turns across crosswalks at unsignalized intersections were designated as “minor”movements to create lower speeds. At unsignalized crossings where high pedestrian volumes wereobserved the corresponding crosswalk link speed was reduced to simulate the slow speeds experienced bydrivers trying to negotiate that crossing.Demand Demand was estimated using Paramics Estimator, which develops origin-destination tables basedon collected data. The best available data was used to estimate demand by origin and destination pair as astarting point for the estimation process; this is referred to as a seed matrix. The seed matrix was based onthe New York Metropolitan Transportation Commission’s Best Practices Model (BPM), which is used toforecast regional travel patterns. Eight different origin destination matrices were estimated, representingdifferent vehicle types and purposes. This was done in order to provide modelers the freedom to adjustindividual demand on different vehicle types and trip purposes independent of other traffic.Route Assignment The choice of assignment methodology is important in a complex urban environment like LowerManhattan where congestion builds and dissipates quickly. Paramics provides three alternative routeassignment methodologies, all-or-nothing, perturbation and dynamic assignment. Alternativemethodologies were assessed. Given the complexity and scale of the modeled network the dynamicfeedback method was found to be essential to accurately replicating route choice and operations in LowerManhattan. Dynamic feedback functions by recalculating route costs at fixed intervals so that familiardrivers may alter their route mid journey.Generalized Cost Individual vehicles choose their route by evaluating the cost of all possible routes and choosingthe one with the lowest cost. Vehicle familiarity factors into the set of possible routes the vehicle canchoose from. For familiar vehicles, the available set of routes contains all possible routes to a destination;for unfamiliar vehicles, the set of routes is restricted to routes composed of links designated as major.Each link in the network is evaluated following a generalized cost formula:Cost = a × T + b × D + c × P Where Cost is in the user cost (minutes), T is time (minutes), D is route length (km), P is the priceof tolls (dollars). The units of coefficients a,b,c are unitless, minutes/km and minutes/dollar respectively.Because cost is in minutes, the time coefficient a is equal to 1. The b coefficient was derived based on theaverage travel speed of 25 mph which translates to 1.5 min/km. The c coefficient is zero because there areno tolls on travel within Lower Manhattan.Cost = 1× T + 1.5 × D + 0 × PFeedback Dynamic feedback is used to model the real-time assessment of travel times. This information ismade available to “familiar” drivers only, and is provided prior to and during their trip at the end of eachupdate period. This technique can be used in conjunction with stochastic assignment to provide a morerobust route-choice model. A feedback period of five minutes was employed, meaning that “familiar” drivers (85% of thetotal) calculate the cost of all available routes every five minutes. With the incorporation of perturbed
Singh, Huey, Lethco, Dunn, Sangavarapu 9stochastic assignment, they might select a route that is not necessarily the shortest. A key objective was tomaximize the feedback period, given that drivers are generally not capable of making key route choicedecisions in short periods. However the longer feedback periods resulted in gridlock occurring during thesimulation. Different feedback periods were tested, with five minutes being the most appropriate in termsof accurately modeling behavior while not making simulation runs computationally onerous. Smoothing functions serve to dampen oscillations in travel time between update periods. There isthe possibility that dynamic feedback can induce large fluctuations in the traffic choosing alternativeroutes after each update. A smoothing factor of 0.70 was used, which results in a weighted averaging of70% of the latest values and 30% of the previously smoothed values. A feedback decay factor keeps a link costs from going to zero immediately, should no car travelalong it during a time step. The default value of 0.995 was chosen resulting in an exceptionally slow rateof decay in cost.CALIBRATION The calibration stage ensures that the model adequately reflects the observed traffic behavior,traffic volume and travel times prior to a more robust and quantitative measure of performance in thevalidation stage. Calibration involved a review of global and local model parameters that relate tonetwork and demand matrix definition and assignment. In addition, the calibration task involved a visualreview of the model operation during assignment using a variety of seeds to ensure the model replicatedtraffic conditions that were observed on-site.Visual Calibration Visual examination of the network during simulation is important as a check on the quantitativemodeling described above. Although the effect on vehicle traffic is taken into account in the networkdesign stage, microsimulation models do not visually model the detailed maneuvers on the congested roadnetwork. Parked vehicles, double-parked vehicles, bicyclists and pedestrians are not visually depicted inthe model. This results in an appearance that the street network may be less congested than it actually is.To address this condition, a structured approach of applying link and node characteristics was taken toreplicate traffic impedances and ensure logical routing. Furthermore, the model is designed to depict a typical day with recurrent congestion.Nonrecurrent congestion such as incidents, break downs or other random events was not within the scopeof the model, although Paramics is capable of modeling these types of incidents if this were desired in thefuture.VALIDATIONCriteria Validation criteria, shown in TABLE 4, focused on volumes, travel times and visual audits. Thecriteria used several metrics: percent difference, R² and GEH statistics. Percent differences were used forscreenlines as they are the coarsest measure of traffic flow in the criteria. R² which measures goodness-of-fit between an estimated and observed value is used for individual link counts and turning movements.The GEH statistic is a standard traffic modeling measure used to evaluate the accuracy of flows givenwide ranges in observed flows across a network. The formula is: 2( M − C ) 2GEH = M +CwhereM = modeled volumeC = observed volume
Singh, Huey, Lethco, Dunn, Sangavarapu 10The criteria were based on previous guidelines, literature (4,7,8,10,12) and available data. Volume statistics were calculated at three levels of detail. This was done so that large flows couldbe validated first, then slowly working toward validating the more detailed movements. Screenline flowsprovide a coarse measurement across major inbound and outbound links in the network. Individual linkflows were measured for 120 intersections in the network. Next, turn movements were validated at criticalintersections where it was important to capture left turning behavior. Travel time measurements wereused to validate 10 different routes through Lower Manhattan.ResultsScreenlines The screenline totals are reported for each direction (i.e. eastbound and westbound or northboundand southbound) for all seven screenlines. As shown in TABLE 5, the total screenline flows were wellwithin the acceptable range of 5 to 10% – no screenline total had a percent difference greater than 7%.Individual Link Flows Individual link flows within each screenline were compared with the results shown in TABLE 5.This included over 120 individual link flow counts. In both the AM and PM peak periods, the individuallink flow results generally met or exceeded the validation targets. For the R² correlation, both periodsproduced results above the targeted range of 0.85 to 0.95. This shows that variability between themodeled volumes and observed volumes is very low and that statistically, the model is in line with theobserved volumes. The percentage of GEH values below 5 for individual links exceeded the 75-80%target in the PM, but was slightly short of the target in the AM. The percentage of GEH values below 10for individual links exceeded the target ranges for the AM and PM periods.Turning Movements Turning movements at key locations on strategic routes were selected for validation. There wereapproximately 320 turning movement counts considered fit for validation, compared to 120 link flows. Itwas important that the turning counts used for validation were consistent with observed and historic databecause they were often used in the matrix estimation process. Because of observed inconsistencies in thedata collection process, several checks were applied to the turning movement counts in order to assurethey had been correctly collected. First intersection counts were compared with adjacent intersections forconsistency. If the count was not consistent with adjacent intersections the count was then compared withhistoric data at the intersection. Counts that were inconsistent with both sources were not used in thedemand estimation or validation processes. The validation results are summarized in TABLE 5. The modeled turning movement volumes are typically difficult to validate against observedcounts because they require large sample sizes in order to reduce variability. As a result, lower validationtargets were set. TABLE 5 shows that both the AM and PM models meet the R² targets for turning movements.In the case of the GEH targets, the AM model results just fall below the target range for GEH less than 5,but the results meet the criteria set for GEH less than 10. The PM model validation meets both GEHtargets. The slightly lower validation results for the AM compared to the PM is most likely due todiscrepancies and flow variability over the modeled period in localized areas. Overall both the AM andPM models provide a good correlation to observed turning movement volumes in the study area,Travel Times The travel times along major corridors were validated based on probe vehicle runs collectedduring the data collection phase. Neither the AM or PM models met the travel time guideline targets,although the AM model produced better results than the PM. In general, the travel time validation may
Singh, Huey, Lethco, Dunn, Sangavarapu 11suggest that vehicles in the model travel faster through the network when compared to observationsduring the survey. However the limited sample size and the difficulty in measuring single travel time runsin Paramics (travel time results were recorded by creating a public transit route to act as a probe,resulting in an underestimation of modeled travel times) are possible explanations for the disparity. Future model work and data collection will focus on strengthening the travel time validation bycollecting a larger dataset and utilizing the capability in the new version of Paramics to measure modeledtravel times.CONCLUSION The experience developing the Lower Manhattan microsimulation model illustrates a practical,planning-based approach to modeling a complex urban transportation network. Beginning at the datacollection phase, detailed information was collected regarding vehicles, transit, pedestrian, parkingbehavior and land use. This data informed the design of the model, as did prior studies of the area, so thatan understanding of streets and neighborhoods informed the network construction. Assignmentparameters were developed based on understanding of the types of vehicles in Lower Manhattan, as wellas the time period being modeled. Validation was found to be a time consuming and complex process. The microsimulationguidelines and standards that were reviewed tended to focus on modeling highways or corridors – notcentral business districts. Because central business districts are unique in regard to network size and users,developing standards and guidelines around these needs would help improve the practice of modelingurban areas. The most difficult validation issue was the inability to validate travel time in the model. Theissue was complicated by the high number of possible routes, high volumes and nonrecurrent congestion.Because of all of these issues, it became apparent that a robust sample of travel times is necessary tobetter understand the variability. The model was designed with the intention of estimating the route changing and travel demandresulting from changes in development and the street network. With these intentions in mind, the model isconsidered valid and accurate.Next Steps The Lower Manhattan model has been used to analyze the impacts resulting from a variety ofproposed street management and development scenarios. Going forward, the Lower Manhattan modelwill be used for a variety of planning tasks including testing the traffic impacts of street changes related todevelopment, pedestrianization and reconfiguration. In addition the Lower Manhattan ConstructionCommand Center intends to integrate the model in to the Lower Manhattan construction schedulingsystem in order to assess traffic impacts of various detour plans. The next phase of the model development will focus on developing a more robust pedestrian andtransit component, with pedestrian agents interacting with vehicles and transit. In addition, the model willexpand further north to encompass Chinatown and the Holland Tunnel areas of Lower Manhattan. Theexpanded model development will involve additional data collection, calibration and validation processes.
References1. Downtown Alliance. Lower Manhattan Fact Sheet 2008 (Q2), 2008.2. “Manhattan: City Report Record Number of Visitors” January 14, 2008. URL:http://www.nytimes.com/2008/01/14/nyregion/14mbrfs-visitors.html?fta=y , Accessed: 10-22-08.3. Chicago Office of Tourism. 2006 Statistical Information, 2006.4. FHWA. Guidelines for Applying Traffic Microsimulation Modeling Software. Prepared by DowlingAssociates. August 2003.5. SIAS. Microsimulation Consultancy Good Practice Guide6. Austroads. The Use and Application of Microsimulation Models, Prepared by ARRB Group. 20067. Dowling, R., Skabardonis, A. et al. Guidelines for Calibration of Microsimulation Models: Frameworkand Applications.Transportation Research Record: Journal of the Transportation Research Board No1876, TRB, National Research Council, Washington D.C. 2004, pp. 1-9.8. Traffic Appraisal in Urban Areas: Highways Agency, Manual for Roads & Bridges, Vol. 12.Department for Transportation, London, May 1996.9. FHWA. Model Validation and Reasonableness Checking Manual.10. Land Transport New Zealand Project Evaluation Manual11. Freeway System Operational Assessment. Technical Report I-33: Paramics Calibration & ValidationGuidelines (Draft). Wisconsin Department of Transportation, District 2, Milwaukee, June 2002.12. Lower Manhattan Development Corporation. Lower Manhattan Bus Study. 2006.13. New York City Department of Transportation. Lower Manhattan Street Management Framework.Prepared by Ove Arup & Partners Consulting Engineers. September 2004.14. New York City Department of City Planning. Pedestrian Level of Service Study, Phase I – Chapter 5.April 2006.15. Highway Capacity Manual. TRB, National Research Council, Washington, D.C., 2000.
Tables and Figures FIGURE 1 The Lower Manhattan simulation study area.
TABLE 1 Vehicle Type ParametersType ID Comment Matrix that type Proportion of Perturbation is applied to Familiarity matrixCar 1 5% 55% 1 38% Car external to externalCar 2 5% 85% 2 100% Car Brooklyn Bridge relatedCar 3 5% 85% 3 88% Car - other zonesCar 4 5% 85% 4 88% Car - on-street zonesCar 5 5% 55% 1 50% Cars assigned to HOV lane out of BBTTaxis 9 5% 85% 6 100% TaxisFHV 10 5% 85% 7 100% Black Cars/LimosMinibus 11 - - Fixed - Fixed route vehicle released to Route collect travel time dataLGV 12 5% 85% 1 12% Light commercial vehicle external to externalLGV 13 5% 85% 3 12% Light commercial vehicle - other zonesLGV 14 5% 85% 4 12% Commercial vehicle - on-street zonesBus 16 - - Fixed - Fixed route bus services assigned Route according to published timetables and surveysCoach 17 5% 25% 8 10% Part of heavy truck matrixOGV 18 5% 25% 8 40% Part of heavy truck matrixLGV 19 5% 25% 8 50% Part of heavy truck matrix
TABLE 4 Validation CriteriaCriteria Targets CommentsScreenline FlowsPercentage difference 5 - 10% Outliers may be accepted depending on confidence of counts and other validation criteria.Individual link flowsR2 0.85 – 0.95 Correlation of all measured to modeled link flows. Should tend toward 0.9.GEH<5 75% - 80% of counts Small difference between modeled and observed for most linksGEH<10 95% of counts No significant outliers, unless justification provided.Turn FlowsR2 0.85 – 0.95 Correlation of all measured to modeled turn flows. Probably tend toward 0.85.GEH<5 65% - 75% of counts Small difference between modeled and observed for most turnsGEH<10 90% of counts A small number of significant outliers allowed, that are shown not to significantly impact on the models fitness for purpose.Travel timeMean difference <15% 85% of routes Difficult to achieve due to the lack of observed travel time information along each route compared to modeledAverage modeled travel 95% of routes Difficult to achieve given travel time variabilitytime within range of in networkobserved times
TABLE 5 Summary of Validation ResultsCriteria Targets Achieved Achieved Comments AM PMScreenline FlowsPercentage 5 – 10% All <6% All <7% AcceptabledifferenceIndividual link flowsR2 0.85 – 0.95 0.99 0.99 AcceptableGEH<5 75% - 80% of counts 74% 84% Acceptable – AM slightly lowGEH<10 95% of counts 96% 98% AcceptableTurn FlowsR2 0.85 – 0.95 0.95 0.98 AcceptableGEH<5 65% - 75% of counts 63% 70% Acceptable – AM slightly lowGEH<10 90% of counts 91% 94% AcceptableTravel timeMean difference 85% of routes 50% 11% Doesn’t achieve<15% targetsAverage modeled 95% of routes 22% 6% Doesn’t achievetravel time within targetsrange of observedtimes