Real-Time Socio-Economic Data for Travel Demand Modeling and Project Evaluation PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Real-Time Socio-Economic Data for Travel Demand Modeling and Project Evaluation PDF full book. Access full book title Real-Time Socio-Economic Data for Travel Demand Modeling and Project Evaluation by Kevin M. Ford. Download full books in PDF and EPUB format.
Author: Kevin M. Ford Publisher: Purdue University Press ISBN: 9781622601028 Category : Transportation Languages : en Pages : 146
Book Description
Small and medium-sized communities often lack data that are complete, current, representative of the community, and appropriate for the travel demand models/software to be applied. To aid such communities, this research was conducted to meet the following objectives: (1) Identify and facilitate the communication of data that is updated more frequently than the Decennial Census for Indiana Travel Demand Models; (2) Allow the use of cross-classification techniques within trip generation for planning organizations that lack the time and resources to conduct travel demand/household surveys; (3) Evaluate the sensitivity of travel demand model outputs to varied parameters and inputs so as to help focus data collection and guide parameter selection; and (4) Apply risk management strategies to travel demand modeling so as to help planners program the most resource efficient projects.
Author: Kevin M. Ford Publisher: Purdue University Press ISBN: 9781622601028 Category : Transportation Languages : en Pages : 146
Book Description
Small and medium-sized communities often lack data that are complete, current, representative of the community, and appropriate for the travel demand models/software to be applied. To aid such communities, this research was conducted to meet the following objectives: (1) Identify and facilitate the communication of data that is updated more frequently than the Decennial Census for Indiana Travel Demand Models; (2) Allow the use of cross-classification techniques within trip generation for planning organizations that lack the time and resources to conduct travel demand/household surveys; (3) Evaluate the sensitivity of travel demand model outputs to varied parameters and inputs so as to help focus data collection and guide parameter selection; and (4) Apply risk management strategies to travel demand modeling so as to help planners program the most resource efficient projects.
Author: Publisher: Transportation Research Board ISBN: 0309214009 Category : Traffic estimation Languages : en Pages : 170
Book Description
TRB’s National Cooperative Highway Research Program (NCHRP) Report 716: Travel Demand Forecasting: Parameters and Techniques provides guidelines on travel demand forecasting procedures and their application for helping to solve common transportation problems.
Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
A comprehensive travel demand modeling methodology using cellular probe data is presented in this thesis, which includes a static daily O-D estimation model, a mode share estimation model and a dynamic (time-dependent) O-D estimation model. At first, the cellular probe trajectories are obtained by recording all the signal-transition events of cellular probes to determine the trip origins and destinations. The ownership of cell phone was treated as a conditional probability depending on users' socio-economic factors available in the census data such as age, race, household income, etc. Thereinafter, the traveling population daily O-D demand was estimated via a robust Horvitz-Thompson estimator. The methodology was tested via a VISSIM simulation and results were compared with a conventional simple random sampling (SRS) method. The comparison outcome shows great potential of using cellular probe data as a means to estimating O-D travel demand. The mode share estimation model consists of two major parts: offline learning and online inference. The offline learning extracts the temporal and speed features from the cellular probe trajectories. The model parameters are calibrated through the offline learning process. The online inference determines the transportation mode for individual cell phone user in a real-time manner. The methodology was tested via a VISSIM based simulation and a case study designed for both the offline learning and online inference parts. The results show the great potential of using the information of cellular probe trajectories as a means to estimating the transportation mode shares. A Kalman filter based dynamic O-D estimation and prediction model is also proposed. Not like the traditional Kalman filter based O-D estimation method, in which the link traffic counts and the link traffic assignment matrix are mostly taken into consideration in the observation equation, this method utilizes the cellular probe counts crossing the cell boundaries as the observed variables and derives an assignment matrix which assigns the cellular probe counts to subsets of the links - those links covered by the cell boundaries. By conducting the simulation and field experiments, it is observed that the model is a feasible means to estimate and predict the dynamic O-D matrix.
Author: Publisher: ISBN: Category : Choice of transportation Languages : en Pages : 200
Book Description
"This issue contains 21 papers concerned with the following aspects of travel demand forecasting: sketch planning; spatial transferability of tour-generation models; discrete choice estimator properties; emission pricing for large transportation networks; probit Bayes estimator in discrete choice modeling; estimating transborder trips integrating binational communities as a single transportation system; estimating random coefficients logit with panel data; joint model of vehicle holdings and primary driver for a household; network equilibrium model with dogit and nested logit structures; assessing traffic measures and policies on reliability of traffic operations and travel time; legislation for innovation in travel demand modeling; evaluating trip distribution models; urban travel time analysis; estimating discrete choice models with incomplete data; within-day replanning of events; accuracy of zonal socioeconomic forecasts; population synthesis in land use microsimulation; mileage-based user fees impact on traveler route choice and network performance; modeling household fleet choice; implementing travel time reliability in forecasting of regionwide travel; and synthesizing household characteristics using a dependence-preserving approach"--Publication information.
Author: Publisher: ISBN: Category : Choice of transportation Languages : en Pages : 694
Book Description
"This course attempts to communicate to travel modeling professionals some of the [travel demand forecasting] procedures developed by their colleagues around the U.S. and abroad, most of which have been implemented as part of an existing travel demand modeling system."--p.1-5
Author: Kevin M. Ford
Author: Kevin M. Ford
Author: 
Author: 
Author: Antti Talvitie
Author: Martin Gomm Richards
Author: Marc P. Kaplan
Author: 
Author: 
Author: 
Author: