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 Smart cities and value of time: An International Symposium

2018 ◽  
Vol 1 (1) ◽  
pp. 71-78
Author(s):  
Karst Geurs ◽  
Francesc Robusté ◽  
Lissy La Paix ◽  
Thais Rangel ◽  
John Pritchard
Keyword(s):  
Travel Time ◽  
Traffic Safety ◽  
Travel Demand ◽  
Smart Cities ◽  
Transport Infrastructure ◽  
Value Of Time ◽  
Activity Time ◽  
Urban Living ◽  
Ict Use ◽  
Living Labs

Travel time and activity time have been treated as separate notions, albeit with some assumed interdependencies. But, previous studies show that people accept noticeable higher travel time ratios when travelling by public transport compared with travelling by car. The cost of travel time is reduced as travel time is converted as activity time (towards the productive use). Furthermore, the impact of a high quality journey is an important issue in lifestyle and distribution of activities. The efficiency, safety and productivity of the journey can be expected to impact the value of time. And, the proportion of travel time saved, which can be used for other activities might have positive effects on the quality of life. In recent years some significant ICT-related innovations have been introduced to large shares of society, but research on the current relation between characteristics of ICT-use and its impact on mobility is limited. Development of smart cities and urban living labs is strongly affected by the ICT use and mobility patterns, as urban living labs co-create new products from collaboration between public, private and civic partnerships. At the same time, in certain self-constrained environments, activity duration has many effects in travel patterns for out-ofhome activities. Use of big data to model travel behaviour might bring important implications for individual mobility. Similarly, traffic safety and management of transport infrastructure are a main concern for government decisions in developing countries. This paper contains a summary of five keynote sessions of the Symposium Smart Cities and Value of Time, that took place in Santo Domingo, Dominican Republic in January 18 and 19 2018. This symposium aimed to contribute to the debate on planning transport system and urban development according to the needs and activities undertaken by citizens of the modern ‘on move’ society. This summary presents five key points of transport research oriented to smart cities and value of time, which can be mentioned as: user’s perspective (equity), data collection and infrastructure (traffic safety and network design). These topics are substantially important to estimate accurate travel demand models, and therefore design efficient transport measures.

scholarly journals Prediction of Commuter’s Daily Time Allocation

2013 ◽  
Vol 25 (5) ◽  
pp. 445-455 ◽  
Author(s):  
Fang Zong ◽  
Jia Hongfei ◽  
Pan Xiang ◽  
Wu Yang
Keyword(s):  
Support Vector Regression ◽  
Travel Time ◽  
Time Allocation ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Model System ◽  
Support Vector ◽  
Activity Duration ◽  
Wide Range ◽  
Duration Prediction

This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

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

Dynamics of Activity Time-of-Day Choice

2017 ◽  
Vol 2665 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Ramin Shabanpour ◽  
Nima Golshani ◽  
Joshua Auld ◽  
Abolfazl (Kouros) Mohammadian
Keyword(s):  
Travel Time ◽  
Decision Process ◽  
Travel Demand ◽  
Choice Model ◽  
Time Of Day ◽  
Demand Model ◽  
Start Time ◽  
Time Duration ◽  
Time Horizons ◽  
Activity Attributes

This study explored travelers’ decision behavior in selecting activity start times. The study examined the problem in the context of the Agent-based Dynamic Activity Planning and Travel Simulation (ADAPTS) activity-based travel demand model for the Chicago, Illinois, metropolitan area. A unique feature of the ADAPTS framework is its consideration of planning horizons for various activity attributes. Naturally, the various attributes of an activity—such as start time, duration, location, party involvement, and mode of travel—can be planned in different time horizons. An attribute that is planned affects the choice of other activity attributes. Therefore, developing a true behavioral time-of-day choice model would not be possible unless the planning order of activity attributes and the dynamics of travelers’ decision-making processes are taken into account. Similarly, it can be argued that there should be fundamental differences in the time-of-day decision process when other attributes of the activity are not yet planned but are to be decided at a later time. The presented time-of-day model aims to capture the dynamics of this decision process by considering the planning time horizons of other attributes of the activity, as well as the outcomes of the decisions. The study adopted the discrete choice approach to model activity timing decisions and a hybrid utility maximization and developed a regret minimization model to account for the heterogeneity of decision rules across choice variables. Analysis of the estimation results and parameter elasticities indicates that higher expected travel time, variations in travel time, and schedule occupancy rates for different time choices can significantly increase the regret value of the corresponding choice and therefore affect the time-of-day choice.

scholarly journals Estimating the Value of Time and Its Application

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Lasmini Ambarwati ◽  
Amelia K Indraistuti ◽  
Pretiwindya Kusumawardhani
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Mode Choice ◽  
Transport Infrastructure ◽  
Value Of Time ◽  
Transport Models ◽  
Travel Time Savings ◽  
Time Savings ◽  
Willingness To Use ◽  
Choice Approach

Values of travel time are essential factors in the design of transport infrastructure. The value of time is used in transport models to monetize travel time related to the socio-economic background of travelers. This paper assesses the estimation of the value of time based on questionnaires distributed to travelers in a preference survey. The mode choice approach is employed to estimate these values dependent on vehicle classes for weekdays and at the weekend. Two of the main conclusions using the mode choice approach are that the value of time for private vehicle users is approximately 1.5 times the value of time for public transport users; and the value of travel time on the weekday is twice that of the weekend. This indicates that public transport passengers have more travel time savings than when they use other modes. Another method, the income approach, arrives at similar values of time as that estimated by the mode choice approach. The willingness to use public transport in weekdays is increasing. As a consequence, public transport should be operated at a higher frequency.

Modeling Demographic Relocation in Response to Climate Risk Factors and Gentrification Displacement Pressures

2021 ◽  
pp. 036119812110363
Author(s):  
Sneha Roy ◽  
Pragun Vinayak ◽  
David Von Stroh
Keyword(s):  
Risk Factors ◽  
Low Income ◽  
Travel Demand ◽  
Climate Adaptation ◽  
Regional Climate ◽  
High Growth ◽  
Climate Risk ◽  
Demand Model ◽  
Modeling Tools ◽  
Planning Processes

Climate risk factors, including wildfire, sea level rise, inland flooding, and extreme heat, as well as gentrification displacement pressures will be primary drivers of migration in the coming years. Travel demand modeling relies on reasonable and appropriate forecasts of demographic totals at the detail of travel analysis zones. Methodologies for developing scenarios in response to individual and combined climate risk factors are described, drawing on work undertaken for the Southern California Association of Governments SoCal Regional Climate Adaptation Framework. Methodologies for developing scenarios in response to gentrification displacement pressures of low-income workers are described, drawing on work carried out for the California Statewide Freight Forecasting and Travel Demand Model. These methodologies leverage modeling tools that are readily available to agencies, allowing for rapid testing of scenarios and integration with other planning processes. Climate adaptation and housing policy, respectively, are currently in need of greater integration and coordination. Future directions are explored to integrate these methodologies and create a combined demographic relocation model, sensitive to both climate risk factors and the affordability and gentrification displacement pressures arising out of shifting demand–supply dynamics and population–job balance in high growth areas.

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