Using Google’s Aggregated and Anonymized Trip Data to Support Freeway Corridor Management Planning in San Francisco, California

2017 ◽  
Vol 2643 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Bhargava Sana ◽  
Joe Castiglione ◽  
Drew Cooper ◽  
Daniel Tischler
Keyword(s):  
San Francisco ◽  
Travel Demand ◽  
Time Of Day ◽  
Performance Measurement System ◽  
Demand Model ◽  
Data Set ◽  
Travel Demand Management ◽  
Management Planning ◽  
Data Source ◽  
Freeway Corridor

With rising urban freeway congestion and limited funds available for highway expansion, it may be essential to manage traffic growth by using high-occupancy toll lanes and other travel demand management (TDM) measures. To prepare for and help guide freeway corridor management planning in the US-101 and I-280 corridors in San Francisco, California, information describing trip origins and destinations by time of day was desired. Observed roadway facility-specific origin–destination (O-D) flows can help researchers to understand spatial distribution of demand and impute willingness to pay, actions that are useful in evaluating various TDM strategies. This paper describes a new passively collected O-D data source—Google’s aggregated and anonymized trip (AAT) data—obtained under Google’s Better Cities program. Aggregate hourly flow matrices for 85 districts covering California’s nine-county Bay Area specific to four freeway segments in San Francisco were obtained. Because AAT data account for only a sample of travelers, Google provides relative flows rather than absolute counts. Linear regression models were estimated to relate relative flows in the AAT data set and observed traffic volumes from the California Department of Transportation’s Performance Measurement System. The models were applied to convert relative flows to trips and derive facility-specific, time-dependent O-D matrices. Comparison of these facility-specific O-D matrices to select link O-D matrices from a regional travel demand model show that there is a higher correlation in terms of productions at origin districts and attractions at destination districts than at the O-D flow level. Some opportunities and limitations of the new data source are discussed, along with recommendations for future research.

scholarly journals Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Rolf Moeckel ◽  
Nico Kuehnel ◽  
Carlos Llorca ◽  
Ana Tsui Moreno ◽  
Hema Rayaprolu
Keyword(s):  
Travel Demand ◽  
Time Budget ◽  
Time Of Day ◽  
Destination Choice ◽  
Trip Generation ◽  
Demand Model ◽  
Agent Based ◽  
Travel Mode Choice ◽  
Travel Time Budget ◽  
Aggregate Nature

The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

Generation of Synthetic Daily Activity-Travel Patterns

1997 ◽  
Vol 1607 (1) ◽  
pp. 154-162 ◽  
Author(s):  
Ryuichi Kitamura ◽  
Cynthia Chen ◽  
Ram M. Pendyala
Keyword(s):  
Daily Activity ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Travel Patterns ◽  
Activity Type ◽  
Individual Activity ◽  
Sequential Approach ◽  
Data Set

Microsimulation approaches to travel demand forecasting are gaining increased attention because of their ability to replicate the multitude of factors underlying individual travel behavior. The implementation of microsimulation approaches usually entails the generation of synthetic households and their associated activity-travel patterns to achieve forecasts with desired levels of accuracy. A sequential approach to generating synthetic daily individual activity-travel patterns was developed. The sequential approach decomposes the entire daily activity-travel pattern into various components, namely, activity type, activity duration, activity location, work location, and mode choice and transition. The sequential modeling approach offers practicality, provides a sound behavioral basis, and accurately represents an individual’s activity-travel patterns. In the proposed system each component may be estimated as a multinomial logit model. Models are specified to reflect potential associations between individual activity-travel choices and such factors as time of day, socioeconomic characteristics, and history dependence. As an example results for activity type choice models estimated and validated with the 1990 Southern California Association of Governments travel diary data set are provided. The validation results indicate that the predicted pattern of activity choices conforms with observed choices by time of day. Thus, realistic daily activity-travel patterns, which are requisites for microsimulation approaches, can be generated for synthetic households in a practical manner.

Travel Demand Modeling and Conformity Determination: Atlanta Regional Commission Case Study

2002 ◽  
Vol 1817 (1) ◽  
pp. 172-176 ◽  
Author(s):  
Guy Rousseau ◽  
Tracy Clymer
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Mode Choice ◽  
Time Of Day ◽  
Traffic Model ◽  
Demand Model ◽  
Primary Input ◽  
Land Use Model ◽  
Home Based ◽  
Travel Demand Model

The Atlanta Regional Commission (ARC) regional travel demand model is described as it relates to its link-based emissions postprocessor. In addition to conformity determination, an overview of other elements is given. The transit networks include the walk and highway access links. Trip generation addresses trip production, trip attraction, reconciliation of productions and attractions, and special adjustments made for Hartsfield Atlanta International Airport. Trip distribution includes the application of the composite impedance variable. In the mode choice model, home-based work uses a logit function, whereas nonwork uses information from the home-based work to estimate modal shares. Traffic assignment includes preparation of time-of-day assignments. The model assigns single-occupancy vehicles, high-occupancy vehicles, and trucks by using separate trip tables. The procedures can accept or prohibit each of the three types of vehicles from each highway lane. Feedback between the land use model and the traffic model is accounted for via composite impedances generated by the traffic model and is a primary input to the land use model DRAM/EMPAL. The land use model is based on census tract geography, whereas the travel demand model is based on traffic analysis zones that are subareas within census tracts. The ARC model has extended the state of the practice by using the log sum variable from mode choice as the impedance measure rather than the standard highway time. This change means that the model is sensitive not only to highway travel time but also to highway and transit costs.

Commuting mode choice in the context of travel demand management (TDM) policies: an empirical investigation in Edmonton, Alberta

2011 ◽  
Vol 38 (4) ◽  
pp. 433-443 ◽  
Author(s):  
Hamid Zaman ◽  
Khandker M. Nurul Habib
Keyword(s):  
Travel Demand ◽  
Mode Choice ◽  
Demand Management ◽  
Travel Behaviour ◽  
Data Set ◽  
Travel Demand Management ◽  
Depth Analysis ◽  
Private Car ◽  
Commuting Mode ◽  
The City

Travel demand management (TDM) for achieving sustainability is now considered one of the most important aspects of transportation planning and operation. It is now a well known fact that excessive use of private car results inefficient travel behaviour. So, from the TDM perspective, it is of great importance to analyze travel behaviour for improving our understanding on how to influence people to reduce car use and choose more sustainable modes such as  carpool, public transit, park & ride, walk, bike etc. This study attempts an in-depth analysis of commuting mode choice behaviour using a week-long commuter survey data set collected in the City of Edmonton. Using error correlated nested logit model for panel data, this study investigates sensitivities of various factors including some specific TDM policies such as flexible office hours, compressed work week etc. Results of the investigation provide profound understanding and guidelines for designing effective TDM policies.

An Integrated, Validated, and Applied Activity-Based Dynamic Traffic Assignment Model for the Baltimore-Washington Region

2018 ◽  
Vol 2672 (51) ◽  
pp. 45-55 ◽  
Author(s):  
Lei Zhang ◽  
Di Yang ◽  
Sepehr Ghader ◽  
Carlos Carrion ◽  
Chenfeng Xiong ◽  
...  
Keyword(s):  
Daily Activity ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Integrated Model ◽  
Time Of Day ◽  
Practical Implementation ◽  
Baseline Scenario ◽  
Demand Model ◽  
Dynamic Traffic

The paper discusses the integration process and initial applications of a new model for the Baltimore-Washington region that integrates an activity-based travel demand model (ABM) with a dynamic traffic assignment (DTA) model. Specifically, the integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council, and DTALite, a mesoscopic DTA model. The integrated model simulates the complete daily activity choices of individuals residing in the model region, including long-term choices, such as workplace location; daily activity patterns, including joint household activities and school escorting; activity location choices; time-of-day choices; mode choices; and route choices. The paper describes the model development and integration approach, including modeling challenges, such as the need to maintain consistency between the ABM and DTA models in terms of temporal and spatial resolution, and practical implementation issues, such as managing model run time and ensuring sufficient convergence of the model. The integrated model results have been validated against observed daily traffic volumes and vehicle-miles traveled (VMT) for various functional classes. A land-use change scenario that analyzes the redevelopment of the Port Covington area in Baltimore is applied and compared with the baseline scenario. The validation and application results suggest that the integrated model outperforms a static assignment-based ABM and could capture behavioral changes at much finer time resolutions.

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.

Analysis of Trip and Stop Duration for Shopping Activities: Simultaneous Hazard Duration Model System

2003 ◽  
Vol 1854 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Karthik K. Srinivasan ◽  
Zhiyong Guo
Keyword(s):  
San Francisco ◽  
Hazard Function ◽  
Travel Demand ◽  
Demand Management ◽  
Trip Duration ◽  
Logistic Distribution ◽  
Travel Demand Management ◽  
Management Measures ◽  
Proposed Model ◽  
Rate Profile

A joint hazard-based model for the analysis of simultaneous (mutually interdependent) duration processes is proposed. The proposed model generalizes independent hazard-based models by accounting for correlations between simultaneous duration processes. Furthermore, the model also permits the use of flexible and variable hazard function parameters to capture realistic features observed empirically in activity duration data (e.g., bimodal peaks). To account for correlated processes (duration processes) that underlie observed stop and trip durations, the proposed model relies on an implicit component of error structure that combines a baseline hazard function (log–logistic distribution) with a mixing (log–normal) distribution. This model is estimated by the simulated maximum-likelihood technique and is used to analyze activity and trip duration for shopping activities. The results highlight the need to account for duration dependence effects in activity–travel durations. Furthermore, hazard-based models that disregard correlation across joint duration processes can provide biased estimates and inaccurate forecasts. Empirical results from San Francisco, California (1996), activity diary data imply that stop and trip durations for shopping activities are positively correlated. The hazard rate profile (shape and intensity) also varies significantly across individuals, suggesting the need for targeted demand management measures. At a substantive level, the results indicate the role of personal, household, and situational attributes on activity and trip duration decisions. These findings and models have important applications in the analysis of activity–travel dimensions of duration and timing and the evaluation of alternate travel demand management measures.

scholarly journals What Matters Most in Transportation Demand Model Specifications: A Comparison of Outputs in a Mid-size Network

2015 ◽  
Author(s):  
T. Donna Chen ◽  
Kara Kockelman ◽  
Yong Zhao
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Mode Choice ◽  
Demographic Data ◽  
Time Of Day ◽  
Demand Model ◽  
Land Use Patterns ◽  
Transportation Demand ◽  
Step Model ◽  
The Impact

This paper examines the impact of travel demand modeling (TDM) disaggregation techniques in the context of medium-sized communities. Specific TDM improvement strategies are evaluated for predictive power and flexibility with case studies based on the Tyler, Texas, network. Results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on outputs. Other strategies shown to impact outputs include adding a logit mode choice model and incorporating a congestion feedback loop. For resource-constrained communities, these results show how model output and flexibility vary for different settings and scenarios.BACKGROUND Transportation directly provides for the mobility of people and goods, while influencing land use patterns and economic activity, which in turn affect air quality, social equity, and investment decisions. Driven by the need to forecast future transportation demand and system performance, Manheim (1979) and Florian et al. (1988) introduced a transportation analysis framework for traffic forecasting using aggregated data that provide the basis for what is known as the four-step model: a process involving trip generation, then trip distribution and mode choice, followed by route choice. Aggregating demographic data at the zone level, the four-step model generates trip productions based on socioeconomic data (e.g., household counts by income and size) and trip attractions primarily based on jobs counts. The model then proportionally distributes trips between each origin and destination (OD) zone pair based on competing travel attractions and impedances, under the assumption that OD pairings with higher travel costs draw fewer trips. Trips between each OD pair are split among a variety of transportation modes, allocating trips to private vehicle, transit, or other

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.

Using Data from a State Travel Demand Model to Develop a Multi-Criteria Framework for Transload Facility Location Planning

2018 ◽  
Vol 2672 (9) ◽  
pp. 12-23 ◽  
Author(s):  
Magdalena I. Asborno ◽  
Sarah Hernandez
Keyword(s):  
Travel Demand ◽  
Transportation Network ◽  
State Level ◽  
Demand Model ◽  
Highway Capacity ◽  
Modal Shift ◽  
Data Source ◽  
Using Data ◽  
Infrastructure Maintenance ◽  
Travel Demand Model

The majority of freight is transported within the U.S. by road. However, the use of alternative modes, such as rail and barge, is associated with lower transportation and infrastructure maintenance costs, release of highway capacity, increased safety, and lower emissions. Thus, there is a latent opportunity for shippers and consumers to benefit from modal shift. In this context, strategically located freight-transfer facilities to improve rail and barge access is key. Moreover, for states with lower commodity tonnages and access to short-line rail and navigable waterways, transload facilities have significant potential to shift freight to underutilized modes. This paper develops a multi-criteria assessment framework to identify strategic locations for transload facilities at the state level. Using a statewide travel demand model (STDM) as the main data source, this framework provides a sketch-planning tool to support decision-making for state Departments of Transportation and economic development agencies. The multi-criteria quantify four measures of facility potential: (a) interaction with the transportation network, (b) amount of freight transported between major freight routes, (c) spatial aggregation, and (d) directionality aggregation. Each criterion is estimated and combined at the county level to produce a multi-criteria score, which defines a county’s potential to support transload movements. Using this score, counties are ranked, and facilities prioritized. The framework is applied to Arkansas and validated using the STDM for base (2010) and forecast (2040) years.

Sign in / Sign up

Export Citation Format

Share Document