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.

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.

Developing Consistent Travel Demand Model Systems as a Building Block for Cooperative Planning: Case Study of San Francisco Bay Area

1997 ◽  
Vol 1606 (1) ◽  
pp. 124-131
Author(s):  
Valerie R. Knepper
Keyword(s):  
San Francisco ◽  
Travel Demand ◽  
San Francisco Bay ◽  
San Francisco Bay Area ◽  
Model Systems ◽  
Demand Model ◽  
Bay Area ◽  
Demand Models ◽  
Cooperative Planning ◽  
Travel Demand Model

The San Francisco Bay Area is characterized by a diverse mixture of urban, suburban, and rural development patterns; multiple jurisdictions with local, state, and federal responsibilities; and a multiplicity of transportation system planners, owners, and operators. The Metropolitan Transportation Commission (MTC), the metropolitan planning organization for the region, is responsible for coordinating transportation for the nine-county region and has a sophisticated set of travel-demand models. California established county-level congestion management programs in 1990, including a requirement for travel-demand model consistency with the regional model. Coordination of the multiple travel-demand model systems that proliferated in the region thus became a significant issue. The cooperative planning approach promoted by MTC through the Bay Area Partnership, and the passage of the Intermodal Surface Transportation Efficiency Act, gave additional impetus to integrating transportation information from multiple agencies, including travel-demand model information. The development of an approach to establishing consistency between the travel-demand model systems in the San Francisco Bay Area is described, as are the immediate and subsequent strategies undertaken.

Parsimonious Modeling and Uncertainty Quantification for Transportation Systems Planning Applied to California High-Speed Rail

2012 ◽  
Vol 2266 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Quentin Noreiga ◽  
Mark McDonald
Keyword(s):  
Uncertainty Quantification ◽  
High Speed ◽  
Travel Demand ◽  
Mode Choice ◽  
Demand Model ◽  
Parameter Uncertainties ◽  
Main Mode ◽  
High Speed Rail ◽  
The Impact ◽  
Travel Demand Model

This paper presents a parsimonious travel demand model (PTDM) derived from a proprietary parent travel demand model developed by Cambridge Systematics (CS) for the California high-speed rail system. The purpose of the PTDM is to reduce computational expense for model simulations, optimization and sensitivity analyses, and other repetitive analyses. The PTDM is used to quantify the significance of parameter uncertainties with the use of mean value first-order second moment methods for uncertainty quantification and sensitivity analysis. The PTDM changes the model resolution of the parent travel demand model from a traffic analysis zone to a county-level analysis. The three-step model contains trip frequency, destination choice, and main mode choice models and is calibrated to match the results of the CS model. The main mode choice model predicts primary mode choice results for car, commercial air, conventional rail, and high-speed rail. The PTDM uses data and models similar to parent models to show how uncertainty in travel demand model predictions can be quantified. This paper does not attempt to assess the reliability of parent model forecasts, and the results should not be used to evaluate uncertainty in the California High-Speed Rail Authority's rider ship and revenue forecasts. However, the uncertainty quantification methodology presented here, when applied to the CS model, can be used to quantify the impact of parameter uncertainty on the forecast results.

scholarly journals Challenges and Opportunities in Applying High-Fidelity Travel Demand Model for Improved Network-Wide Traffic Estimation: A Review and Discussion

2014 ◽  
Vol 8 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Riad Mustafa ◽  
Ming Zhong
Keyword(s):  
Travel Demand ◽  
Traffic Volume ◽  
Volume Estimation ◽  
High Fidelity ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand Models ◽  
Challenges And Opportunities ◽  
Traffic Estimation ◽  
Travel Demand Model

Abstract: Estimating traffic volume at a link level is important to transportation planners, traffic engineers, and policy makers. More specifically, this vital parameter has been used in transportation planning, traffic operations, highway geometric design, pavement design, and resource allocation. However, traditional factor approach, regression-­‐based models, and artificial neural network models failed to present network-­‐wide traffic volume estimates because they rely on traffic counts for model development, and they all have inherent weaknesses. A review to previous research work and the state-­‐of-­‐practice clearly indicates that the Traditional Four-step Travel Demand Model (TFTDM) was generally based on large traffic analysis zones (TAZs) and networks consisting of high functional-class roads only. Consequently, this conventional modeling framework yielded a limited number of link traffic assignments with fairly high estimation errors. In the light of these facts and the obvious need of accurate network-wide traffic estimates, this review is conducted. In particular, this paper provides an extensive review of using traditional travel demand models for improved network-­‐wide traffic volume estimation. The paper then focuses on the challenges and opportunities in achieving high-fidelity travel demand model (HFTDM). This review has revealed that, opportunities in relation to both technological advances and intelligent data present a substantial potential in developing the proposed HFTDM for a much more accurate traffic estimation at a network-­‐wide level. Finally, the paper concludes with key findings from the review and provides a few recommendations for future research related to the topic.

scholarly journals What Types of Cars Will We Be Driving? Methods of Forecasting Car Travel Demand by Vehicle Type

2018 ◽  
Vol 2672 (49) ◽  
pp. 125-134 ◽  
Author(s):  
Tudor Mocanu
Keyword(s):  
Autonomous Vehicles ◽  
Travel Demand ◽  
New Technologies ◽  
Autonomous Driving ◽  
Discrete Choice Models ◽  
Demand Model ◽  
Vehicle Type ◽  
Incremental Model ◽  
The Impact ◽  
Travel Demand Model

New technologies are emerging in the private vehicle market. Conventional propulsion systems are set to be replaced by alternative, more environment-friendly ones (e.g., electric vehicles), and certain new features, like autonomous driving, will possibly change the way private cars are employed. To assess the impact of such technologies, one must estimate how often and for which trips these vehicle types will be used. Another issue is the exact localization of certain vehicle types on the network, to assess environmental effects and identify where specific roadside infrastructure (e.g., charging stations) will be required. This paper presents four approaches to forecasting car usage by vehicle type using a macroscopic travel demand model in combination with a vehicle fleet or technology diffusion model. Integrating the two types of models requires tools ranging from assumptions and extrapolation of empirical data to synthetic or incremental discrete choice models. The approaches are employed in a case study forecasting travel demand using privately owned autonomous vehicles (AVs) in Germany in 2030. Despite identical input data, the estimated proportion of vehicle miles traveled (VMT) using AVs varies between 11% and 23% of overall car VMT, depending on the approach chosen. The reasons for this variation in results are investigated and some recommendations are given. To avoid the difficulties of fitting a synthetic model to observed data and to increase the accuracy of the results, the recommendation is to formulate the vehicle type choice as an incremental model added to the travel demand model.

scholarly journals Uncertainty Quantification of the Traffic Assignment Model

2020 ◽  
Author(s):  
Mundher Seger ◽  
Lajos Kisgyörgy
Keyword(s):  
Uncertainty Quantification ◽  
Traffic Flow ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Posterior Distributions ◽  
Demand Model ◽  
Assignment Model ◽  
Wide Range ◽  
Least Squares Adjustment ◽  
Travel Demand Model

Forecasting of traffic flow in the traffic assignment model suffered to a wide range of uncertainties arising from different sources and exacerbating through sequential-stages of the travel demand model. Uncertainty quantification can provide insights into the level of confidence on the traffic assignment model outputs, and also identify the uncertainties of the input Origin-Destination matrix for enhancing the forecasting robustness of the travel demand model. In this paper, a systematic framework is proposed to quantify the uncertainties that lie in the Origin-Destination input matrix. Hence, this study mainly focuses on predicting the posterior distributions of uncertainty Origin-Destination pairs and correcting the biases of Origin-Destination pairs by using the inverse uncertainty quantification formulated through Least Squares Adjustment method. The posterior distributions are further used in the forward uncertainty quantification to quantify the forecast uncertainty of the traffic flow on a transport network. The results show the effectiveness of implementing the inverse uncertainty quantification framework in the traffic assignment model. And demonstrate the necessity of including uncertainty quantification of the input Origin-Destination matrix in future work of travel demand modelling.

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.

scholarly journals Determining service provider and transport system related effects of ridesourcing services by simulation within the travel demand model mobiTopp

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Gabriel Wilkes ◽  
Lars Briem ◽  
Michael Heilig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
...  
Keyword(s):  
Transport System ◽  
Travel Demand ◽  
Waiting Times ◽  
Demand Model ◽  
Agent Based ◽  
Demand Models ◽  
Decision Making Processes ◽  
The City ◽  
As Effects ◽  
Travel Demand Model

Abstract Purpose Ridesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation. Method This paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data. Results and Discussion In this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services.

Sign in / Sign up

Export Citation Format

Share Document