Method of Successive Averages Versus Evans Algorithm: Iterating a Regional Travel Simulation Model to the User Equilibrium Solution

1998 ◽  
Vol 1645 (1) ◽  
pp. 32-40 ◽  
Author(s):  
W. Thomas Walker ◽  
Thomas F. Rossi ◽  
Nazrul Islam
Keyword(s):  
Performance Monitoring ◽  
Travel Demand ◽  
User Equilibrium ◽  
Model System ◽  
Demand Model ◽  
Initial Model ◽  
Vehicle Miles Traveled ◽  
Convergence Error ◽  
Iteration Methods ◽  
Method Of Successive Averages

The results of comparative tests of two methods for iterating a regional travel demand model system are presented. Model iteration is necessary to ensure consistency between model input and output speeds, as required by current federal legislation. Two methods were tested: the Evans algorithm and the method of successive averages. A series of tests using alternative assignment techniques was conducted for each method. Criteria for evaluating the iteration methods included convergence error, average highway speeds compared with observations, highway vehicle miles traveled compared with Highway Performance Monitoring System estimates, transit boardings compared with observations, and computer running time. It was concluded that the Evans algorithm performed the best, primarily on the basis of superior computational efficiency, although good results were obtained by using the method of successive averages. Use of the Evans algorithm is recommended, embedded within a formal assignment restart, for iterating the model system. Multiple iterations of highway assignment should be used in the initial model loop and all-or-nothing assignments in subsequent iterations of the modeling chain.

scholarly journals An agent-based transportation impact sketch planning (TISP) model system

2021 ◽  
Vol 14 (1) ◽  
pp. 219-253
Author(s):  
Ayad Hammadi ◽  
Eric J Miller
Keyword(s):  
Land Use ◽  
Architectural Design ◽  
Travel Demand ◽  
Transportation Network ◽  
Geographic Location ◽  
Model System ◽  
Demand Model ◽  
Control Variables ◽  
Agent Based ◽  
Traffic Impact

A traffic impact sketch planning (TISP) model is presented for the estimation of the likely travel demand generated by a major land-use development or redevelopment project. The proposed approach overcomes the problems with the non-behavioral transportation-related studies used in practice for assessing the development design impacts on the local transportation system. The architectural design of the development, in terms of the number and type of dwellings, by number of bedrooms per unit, and the land-use categories of the non-residential floorspace, are reflected in the TISP model through an integrated population and employment synthesis approach. The population synthesis enables the feasible deployment of an agent-based microsimulation (ABM) model system of daily activity and travel demand for a quick, efficient, and detailed assessment of the transportation impacts of a proposed neighborhood or development. The approach is not restricted to a certain type of dataset of the control variables for the geographic location of the development. Datasets for different geographic dimensions of the study area, with some common control variables, are merged and cascaded into a synthesized, disaggregate population of resident persons, households and jobs. The prototype implementation of the TISP model is for Waterfront Toronto’s Bayside Development Phase 2, using the operational TASHA-based GTAModel V4.1 ABM travel demand model system. While the conventional transportation studies focus on the assessment of the local traffic impacts in the immediate surroundings of the development, the TISP model investigates and assesses many transportation related impacts in the district, city, and region, for both residents and non-residents of the development. TISP model analysis includes the overall spatiotemporal trips distribution generated by the residents and non-residents of the development for the auto and non-auto mobility systems and the simulated agents diurnal peaking travel times. The model results are compared with the trips estimates by a prior project traffic impact study and the Institute of Transportation Engineers (ITE) Trip Generation Manual (TGM) rates of weekday trips for the relevant land uses. Future extensions and improvements of the model including the generalization and full automation of the model, and the bi-level macro-micro representation of the transportation network are also discussed.

Travel demand model system for the information era

1996 ◽  
Vol 23 (3) ◽  
Author(s):  
Moshe Ben-Akivai ◽  
JohnL. Bowman ◽  
Dinesh Gopinath
Keyword(s):  
Travel Demand ◽  
Model System ◽  
Demand Model ◽  
Travel Demand Model

scholarly journals An Integrated Pipeline Architecture for Modeling Urban Land Use, Travel Demand, and Traffic Assignment

2018 ◽  
Author(s):  
Paul Waddell ◽  
Geoff Boeing ◽  
Max Gardner ◽  
Emily Porter
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Urban Land ◽  
User Equilibrium ◽  
Urban Land Use ◽  
Analysis Tool ◽  
Demand Model ◽  
Pipeline Architecture ◽  
Assignment Model

Integrating land use, travel demand, and traffic models represents a gold standard for regional planning, but is rarely achieved in a meaningful way, especially at the scale of disaggregate data. In this report, we present a new pipeline architecture for integrated modeling of urban land use, travel demand, and traffic assignment. Our land use model, UrbanSim, is an open-source microsimulation platform used by metropolitan planning organizations worldwide for modeling the growth and development of cities over long (~30 year) time horizons. UrbanSim is particularly powerful as a scenario analysis tool, enabling planners to compare and contrast the impacts of different policy decisions on long term land use forecasts in a statistically rigorous way. Our travel demand model, ActivitySim, is an agent-based modeling platform that produces synthetic origin--destination travel demand data. Finally, we use a static user equilibrium traffic assignment model based on the Frank-Wolfe algorithm to assign vehicles to specific network paths to make trips between origins and destinations. This traffic assignment model runs in a high-performance computing environment. The resulting congested travel time data can then be fed back into UrbanSim and ActivitySim for the next model run. This technical report introduces this research area, describes this project's achievements so far in developing this integrated pipeline, and presents an upcoming research agenda.

Pricing and Reliability Enhancements in the San Diego, California, Activity-Based Travel Model

2017 ◽  
Vol 2669 (1) ◽  
pp. 19-30
Author(s):  
Nagendra Dhakar ◽  
Joel Freedman ◽  
Mark Bradley ◽  
Wu Sun
Keyword(s):  
Travel Time ◽  
San Diego ◽  
Travel Demand ◽  
Model System ◽  
Transport Infrastructure ◽  
Value Of Time ◽  
Regression Equations ◽  
Demand Model ◽  
Modeling Tools ◽  
Automobile Travel

The estimation of demand for priced highway lanes is becoming increasingly important to agencies seeking to improve mobility and find alternative revenue sources for the provision of transportation infrastructure. However, many modeling tools fall short of what is required for robust estimates of demand with respect to toll and managed lanes in two key areas: the value of time is often aggregate and not consistently defined throughout the model system, and the reliability of transport infrastructure is rarely considered. This paper describes an effort that implemented recommendations of the Strategic Highway Research Program on pricing and reliability within a regional activity-based modeling system for the San Diego, California, region. The implemented recommendations included distributed travel time sensitivities across the synthetic population and special travel markets, continuous cost sensitivity on the basis of income, and multiple value of time bins in highway skimming and assignment. The work also included innovative research related to the analysis of travel time variability on the basis of a temporally disaggregate (1-min interval) data set of automobile travel speeds for most automobile links in the San Diego network for the month of October 2012. Regression equations that related the travel time reliability to link characteristics, incorporated reliability in automobile travel skims, incorporated those skims in the travel demand model system, and calculated toll elasticity on toll roads in San Diego County were estimated. The enhanced model matched observed toll demand better than the original model. Resulting elasticity values were generally found to be in the ranges reported in the literature.

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

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.

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

2020 ◽  
Vol 2674 (6) ◽  
pp. 363-371
Author(s):  
Jungin Kim ◽  
Ikki Kim ◽  
Jaeyeob Shim ◽  
Hansol Yoo ◽  
Sangjun Park
Keyword(s):  
Travel Behavior ◽  
Household Income ◽  
Travel Demand ◽  
Behavioral Model ◽  
Income Group ◽  
Air Travel ◽  
Jeju Island ◽  
Demand Model ◽  
Air Travel Demand ◽  
Travel Demand Model

The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.

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