Model-Based Long-Range Transportation Planning Tool for New Jersey

2002 ◽  
Vol 1817 (1) ◽  
pp. 93-101
Author(s):  
Anthony J. De John ◽  
Robert Miller ◽  
Kyle B. Winslow ◽  
Jennifer J. Grenier ◽  
Deborah A. Cano
Keyword(s):  
New Jersey ◽  
Long Range ◽  
Travel Demand ◽  
Management Strategies ◽  
Transportation System ◽  
Transportation Systems ◽  
Analysis Tool ◽  
Demand Model ◽  
Demand Models ◽  
The Impact

The New Jersey Department of Transportation (NJDOT) updates its long-range transportation plan every 5 years. The plan sets forth strategies, provides a framework for directing investment, and identifies financial resources needed to sustain the plan’s vision. Setting the direction of a long-range transportation program revolves around forecasting future transportation conditions and managing investments to address future needs. An analysis tool was needed to help assess the impact of growth on the statewide transportation system and predict system performance based on multimodal strategic investments. The development and use of an analysis tool based on a travel demand model to assess congestion and mobility issues in 2025 are described. The analysis tool linked the state’s three metropolitan planning organization (MPO) regional travel demand models to perform a statewide assessment. Although the models were run independently, methods were developed to provide a common basis for forecasting future travel conditions. The models used MPO-generated trend-based growth in population and employment through 2025. Multimodal transportation supply and demand strategies, including transit improvements, capacity improvements, transportation demand management strategies, and intelligent transportation systems-transportation system management strategies, were simulated and tested to assess what types and combinations of improvements would be needed to relieve congestion and improve mobility. The tool proved very helpful in defining transportation needs and providing input to a financial assessment. The testing indicated that no single strategy is likely to improve future travel conditions, but a combination of multimodal strategies offers significant improvements over congestion levels predicted for 2025 if no improvements are made.

Accuracy and Performance of Improved Speed-Flow Curves

1998 ◽  
Vol 1646 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Richard G. Dowling ◽  
Rupinder Singh ◽  
Willis Wei-Kuo Cheng
Keyword(s):  
Travel Demand ◽  
Traffic Assignment ◽  
Demand Model ◽  
Power Functions ◽  
Flow Curves ◽  
Speed Flow ◽  
Demand Models ◽  
Higher Power ◽  
The Impact ◽  
Travel Demand Model

Skabardonis and Dowling recommended updated Bureau of Public Road speed-flow curves for freeways and signalized arterials to improve the accuracy of speed estimates used in transportation demand models. These updated curves generally involved the use of higher power functions that show relatively little sensitivity to volume changes until demand exceeds capacity, when the predicted speed drops abruptly to a very low value. Skabardonis and Dowling demonstrated that the curves provide improved estimates of vehicle speeds under both uncongested and queueing conditions; however, they did not investigate the impact of these curves on the performance of travel demand models. Practitioners have been concerned about the impacts of such abrupt speed-flow curves on the performance of their travel demand models. Spiess has stated that higher power functions are more difficult computationally for computers to evaluate and that more abrupt speed-flow curves adversely affect the rate of convergence to equilibrium solutions in the traffic assignment process. In this paper the impact of the Skabardonis and Dowling updated speed-flow curves on the performance of selected travel demand models is investigated. The updated speed-flow curves were found to significantly increase travel demand model run times. However, it is demonstrated that an alternative speed-flow equation developed by Akçelik has similar or better accuracy and provides much superior convergence properties during the traffic assignment process. The Akçelik curve significantly reduced travel demand model run times.

Exploratory Modeling and Analysis for Transportation: An Approach and Support Tool - TMIP-EMAT

2019 ◽  
Vol 2673 (9) ◽  
pp. 407-418 ◽  
Author(s):  
Martin Milkovits ◽  
Rachel Copperman ◽  
Jeffrey Newman ◽  
Jason Lemp ◽  
Thomas Rossi ◽  
...  
Keyword(s):  
Travel Demand ◽  
Supply And Demand ◽  
Single Point ◽  
Transportation Systems ◽  
Analysis Tool ◽  
Demand Model ◽  
Improvement Program ◽  
Modeling And Analysis ◽  
Forecasting Models ◽  
Travel Forecasting

Traditionally, travel forecasting models have been used to provide single point predictions. That is, a single future scenario is developed and the model is applied to that scenario. This approach, however, ignores the deep uncertainty that exists in future land use, demographic, and transportation systems inputs, not to mention the uncertainty that exists in the model itself. More importantly, transportation policy decisions made on the basis of such model outputs may be misguided and ineffective. This paper demonstrates and motivates the use of travel forecasting models in an exploratory manner that accounts for the inherent uncertainties of the future. Specifically, this paper describes the user workflow for a new planning and modeling tool: the Travel Model Improvement Program Exploratory Modeling and Analysis Tool (TMIP-EMAT) that has been developed to facilitate the use of exploratory techniques with travel forecasting models. Examples from the proof of concept deployment using the Greater Buffalo-Niagara Regional Transportation Council regional travel demand model are included. The goal of the longer term study is to provide TMIP-EMAT for state and regional transportation planning agencies to assess how technological innovations will affect traffic and transit demand on major corridors 20 to 30 years down the road. The tool will illuminate interactions between transportation supply and demand on urban surface transportation systems (especially at the corridor level) through exploratory modeling and simulation, and facilitate insights into potential, possible, plausible, probable or preferred futures.

Regional Bicycle Network Evaluation and Strategic Planning: A Quantitative Methodological Approach Despite Limited Data Sources for Cycling

2021 ◽  
pp. 036119812110288
Author(s):  
Alex van Dulmen ◽  
Martin Fellendorf
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Methodological Approach ◽  
Data Availability ◽  
Data Sources ◽  
Test Case ◽  
Demand Model ◽  
Limited Data ◽  
Demand Models ◽  
Bicycle Infrastructure

In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as an evaluation of the existing network or the benefits of new investments is rarely possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set, and the resulting link and flow volumes are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. For example, our test case, a city in Austria with 300,000 inhabitants, showed that a shift of short trips in the inner city toward cycling would, without countermeasures, provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.

scholarly journals Addressing Household Income Distribution in Air Travel Demand Models: Case Study of the Baltimore–Washington Region

2019 ◽  
Vol 2673 (1) ◽  
pp. 491-502 ◽  
Author(s):  
Geoffrey D. Gosling ◽  
David Ballard
Keyword(s):  
Income Distribution ◽  
Household Income ◽  
Travel Demand ◽  
Metropolitan Region ◽  
Annual Data ◽  
Demand Model ◽  
Passenger Demand ◽  
Average Household Income ◽  
Demand Models ◽  
The Future

The paper describes the development of an air passenger demand model for the Baltimore–Washington metropolitan region that was undertaken as part of a recently concluded ACRP project that explored the use of disaggregated socioeconomic data in air passenger demand studies. The model incorporated a variable reflecting the change in household income distribution, together with more traditional aggregate causal variables: population, employment, average household income, and airfares as measured by the average U.S. airline yield, as well as several year-specific dummy variables. The model was estimated on annual data for the period 1990 to 2010 and obtained statistically significant estimated coefficients for all variables, including both the average household income and the household income distribution variable. Including household income distribution in the model resulted in a significant change to the estimated coefficient for average household income, giving a much higher estimated elasticity of demand with respect to average household income compared with a model that does not consider changes in household income distribution. This has important implications for the use of such demand models for forecasting, as household income distribution and average household income may change in the future in quite different ways, which would affect the future levels of air passenger travel projected by the models.

Transferability of aggregate intercity total demand models

1995 ◽  
Vol 22 (2) ◽  
pp. 283-291
Author(s):  
Amal S. Kumarage ◽  
S. C. Wirasinghe
Keyword(s):  
Sri Lanka ◽  
Public Transportation ◽  
Travel Demand ◽  
Contextual Factors ◽  
Demand Model ◽  
Aggregate Model ◽  
Demand Models ◽  
Intercity Travel ◽  
Model Transfer ◽  
Contextual Characteristics

Over the last 15 years, extensive research has been done on the transferability of travel demand models. However, much of this work has been concentrated towards investigating the transferability of disaggregate mode choice models. The transferability of an aggregate total demand model for intercity travel is examined. Model transfer is possible only when a number of preconditions for transferability are satisfied. One of the principal obstacles to the successful transfer of intercity demand models is the inability to overcome the contextual differences between calibration and application. Here, the components of the intercity total demand model are separated into exogenous and intrinsic (contextual) factors. The latter is thereafter classified as being either transferable or nontransferable. It is shown that transferable attributes can accompany a model during transfer. Nontransferable attributes, on the other hand, will free the model of city or city-pair specific contextual characteristics which should not be transferred to other city pairs. The issues involved in transferring an aggregate model are also investigated. Aggregate data on interdistrict travel by public transportation in Sri Lanka have been used to successfully calibrate a total demand model with a number of transferable and nontransferable attributes that represent both temporal and spatial contextual factors. It is shown that the forecasting ability of this model is far superior to a counterpart model without the intrinsic variables. Key words: travel demand, aggregate, forecasting, transferability, intercity, Sri Lanka.

scholarly journals Impact assessment of short-term management measures on travel demand

2020 ◽  
Vol 53 (1) ◽  
pp. 37-52
Author(s):  
Jinit J. M. D’Cruz ◽  
Anu P. Alex ◽  
V. S. Manju ◽  
Leema Peter
Keyword(s):  
Travel Demand ◽  
Public Transit ◽  
Demand Management ◽  
Simulation Software ◽  
Demand Model ◽  
Short Term ◽  
Travel Demand Management ◽  
Management Measures ◽  
The Impact ◽  
Travel Demand Model

Travel Demand Management (TDM) can be considered as the most viable option to manage the increasing traffic demand by controlling excessive usage of personalized vehicles. TDM provides expanded options to manage existing travel demand by redistributing the demand rather than increasing the supply. To analyze the impact of TDM measures, the existing travel demand of the area should be identified. In order to get quantitative information on the travel demand and the performance of different alternatives or choices of the available transportation system, travel demand model has to be developed. This concept is more useful in developing countries like India, which have limited resources and increasing demands. Transport related issues such as congestion, low service levels and lack of efficient public transportation compels commuters to shift their travel modes to private transport, resulting in unbalanced modal splits. The present study explores the potential to implement travel demand management measures at Kazhakoottam, an IT business hub cum residential area of Thiruvananthapuram city, a medium sized city in India. Travel demand growth at Kazhakoottam is a matter of concern because the traffic is highly concentrated in this area and facility expansion costs are pretty high. A sequential four-stage travel demand model was developed based on a total of 1416 individual household questionnaire responses using the macro simulation software CUBE. Trip generation models were developed using linear regression and mode split was modelled as multinomial logit model in SPSS. The base year traffic flows were estimated and validated with field data. The developed model was then used for improving the road network conditions by suggesting short-term TDM measures. Three TDM scenarios viz; integrating public transit system with feeder mode, carpooling and reducing the distance of bus stops from zone centroids were analysed. The results indicated an increase in public transit ridership and considerable modal shift from private to public/shared transit.

scholarly journals Method of Obtaining Consumer Welfare from Regional Travel Demand Models

1998 ◽  
Vol 1649 (1) ◽  
pp. 81-85 ◽  
Author(s):  
Caroline J. Rodier ◽  
Robert A. Johnston
Keyword(s):  
Travel Demand ◽  
Consumer Welfare ◽  
The United States ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Demand Model ◽  
Compensating Variation ◽  
Demand Models ◽  
High Occupancy Vehicle Lanes

The need for more comprehensive traveler welfare measures is highlighted by the U.S. Intermodal Surface Transportation Efficiency Act (1991) requirement that transportation projects and plans be evaluated for economic efficiency. However, to date, there has been a discrepancy between this requirement and the methods used by regional transportation organizations to evaluate transportation policies in the United States. Kenneth Small and Harvey Rosen illustrate how a consumer welfare measure known as compensating variation can be obtained from discrete choice models. A method of application is developed for the mode choice models in the Sacramento Regional Travel Demand Model. The results of the method’s application to the model for light rail transit, high-occupancy vehicle lanes, and auto pricing scenarios are examined for both total consumer welfare and consumer welfare by income class.

Statewide Assessment of Broader Economic Benefits from Transportation Investment: Integration of Travel Demand Models and SHRP 2 Project C11 Tools in Maryland

2015 ◽  
Vol 2531 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Eirini Kastrouni ◽  
Elham Shayanfar ◽  
Paul M. Schonfeld ◽  
Subrat Mahapatra ◽  
Lei Zhang
Keyword(s):  
Travel Demand ◽  
Public Investment ◽  
Economic Benefits ◽  
Federal State ◽  
Travel Time Reliability ◽  
Demand Model ◽  
Investment Projects ◽  
Demand Models ◽  
State And Local ◽  
Local Agencies

Project selection and prioritization are of utmost importance to federal, state, and local agencies and should be performed cautiously on the basis of expected project costs and benefits. Informed resource allocation decisions with respect to project candidates not only maximize public investment benefits but create economic opportunities and ultimately improve quality of life. With the use of tools readily available to most state agencies (e.g., travel demand models), along with the open-source SHRP 2 Project C11 tools, planners and engineers can proceed with informed statewide assessments of investment projects that yield benefits in market accessibility, travel time reliability, and connectivity. In this study, a seven-level framework was proposed to integrate a travel demand model with the SHRP 2 Project C11 tools and to showcase its functionality with the Intercounty Connector (ICC) MD-200 in Maryland as a case study. After a customized version of the SHRP 2 tools was developed in which Maryland-specific values were used in lieu of the default SHRP 2 parameters, the results suggested that, in the year 2030, a total increase of approximately 1% in buyer–supplier market accessibility would be achieved in the counties that surrounded the ICC as a result of the new construction. Also, all three corridors parallel to the ICC, which served similar origin–destination pairs, would experience a decrease in recurring and incident delays attributable to the ICC. In dollar terms, the value of the total annual benefits from the ICC construction in the year 2030 would amount to approximately $200 million.

Impacts of Model Resolution on Transportation Network Criticality Rankings

2017 ◽  
Vol 2653 (1) ◽  
pp. 93-100 ◽  
Author(s):  
Jonathan Dowds ◽  
Karen Sentoff ◽  
James L. Sullivan ◽  
Lisa Aultman-Hall
Keyword(s):  
Travel Demand ◽  
Transportation Network ◽  
Network Connectivity ◽  
Network Robustness ◽  
Demand Models ◽  
Lower Volume ◽  
Travel Delay ◽  
The Impact ◽  
Aggregate Nature ◽  
Demand Aggregation

Objective rankings of the criticality of transportation network infrastructure are essential for efficiently allocating limited adaptation resources and must account for network connectivity and travel demand. Road link criticality can be quantified by the total travel delay caused when the capacity of a road segment or link is disrupted or removed. These methods can use standard travel demand models, but the exclusion of lower-volume roads and the aggregate nature of traffic analysis zones may distort resulting criticality rankings. To test the impact of link exclusion and demand aggregation, the authors applied the network robustness index, a well-established link criticality measure, to a hypothetical network with varying levels of network resolution and demand aggregation. The results show a statistically significant change in criticality rankings when demand is aggregated and especially when links are excluded from the network, suggesting that criticality rankings may be distorted when estimated with typical demand models. Application to a road network in Vermont supports the finding on the impact of network resolution on criticality rankings.

Updating of parameters in aggregate total demand models

1992 ◽  
Vol 19 (2) ◽  
pp. 236-244
Author(s):  
A. S. Kumarage ◽  
S. C. Wirasinghe
Keyword(s):  
Sri Lanka ◽  
Travel Demand ◽  
Model Updating ◽  
Common Factor ◽  
Parameter Estimates ◽  
Calibration Model ◽  
Demand Model ◽  
Demand Models ◽  
Sample Data ◽  
Model Transfer

Research on demand-model transferability has consistently shown that the updated models perform better than the simple transfer of the original model with the original coefficients. Several methods are available for the updating of parameter estimates during model transfer. The scalar factor method has been extended to specify individual factors for each variable. This method allows the flexibility of removing insignificant variables in transfer; it also permits the grouping of parameters that have to be updated by a common factor. Individual scalar factors can also be identified for variables that are uniquely affected during transfer. This approach therefore incorporates the strength of both the sample data and the calibration model to its maximum showing that this method gives excellent fit to observed flows when tested for geographical transferability of an aggregate intercity total demand model for public transport in Sri Lanka. It is also shown that the Bayesian method becomes less efficient when sample sizes available for updating become smaller. Key words: travel, demand model, updating, transferability, Sri Lanka.

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