Are Static and Dynamic Planning Approaches Complementary? A Case Study for Travel Demand Management Measures

2006 ◽  
pp. 252-265
Author(s):  
Oscar Sánchez ◽  
André de Palma
Keyword(s):  
Travel Demand ◽  
Demand Management ◽  
Travel Demand Management ◽  
Dynamic Planning ◽  
Management Measures ◽  
Planning Approaches

Selection and Evaluation of Travel Demand Management Measures

1997 ◽  
Vol 1598 (1) ◽  
pp. 49-60 ◽  
Author(s):  
Christopher J. Taylor ◽  
Linda K. Nozick ◽  
Arnim H. Meyburg
Keyword(s):  
New York ◽  
Travel Demand ◽  
Demand Management ◽  
Evaluation Tool ◽  
Travel Demand Management ◽  
Additional Information ◽  
Management Measures ◽  
Transportation Modes ◽  
Surface Transportation

Travel demand management (TDM) measures are designed to alter the attractiveness of competing travel modes to prompt individuals to carpool or use transit instead of driving alone. Determining the best set of measures for a given area and estimating the effectiveness of the selected measures involve understanding the characteristics of the available transportation modes and of the area's travelers. The process of developing the best, comprehensive set of TDM measures for the Syracuse, New York, area and predicting the effect of those measures are described. Based on a case study of the best TDM measures and their effect in Syracuse, a procedure is presented that can be used for similar studies elsewhere. An effort is made to use data that would be available for similar studies. The evaluation tool is one that would be available in any other area. The main source of information about the travel patterns was census journey-to-work information. Additional information about employment, transit service, roadway congestion, and so forth was derived from planning reports developed by the local metropolitan planning organization. Similar reports should be available in other areas because of the strict planning provisions of the Intermodal Surface Transportation Efficiency Act of 1991. The major conclusion was that it is indeed possible to select an appropriate set of TDM measures for a given study area while relying on only limited, readily available data and tools.

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 Quick-Scan Appraisal Method to Determine Cost-Effectiveness of Traffic and Demand Management Measures

2013 ◽  
Vol 2359 (1) ◽  
pp. 36-43
Author(s):  
Henk Meurs ◽  
Bert van Wee ◽  
Jan Perdok ◽  
Serge Hoogendoorn
Keyword(s):  
Cost Effectiveness ◽  
Travel Demand ◽  
Demand Management ◽  
Management Program ◽  
Travel Demand Management ◽  
Data Intensive ◽  
Expert Opinions ◽  
Management Measures ◽  
Pricing Measure ◽  
The Cost

This paper presents a quick-scan approach to assess the cost-effectiveness of smaller and poorly demarcated transportation measures; the approach can be used as an initial scan while packages are established to solve specific transportation problems. This paper adds to the available evaluation literature and relies on a combination of expert opinions and simple models rather than on data-intensive, four-stage transportation models. The approach consists of five steps and yields an assessment of the cost-effectiveness of the measure that is being evaluated. As an illustration of this approach, the cost-effectiveness of a pricing measure within a large Dutch travel demand management program was determined to illustrate the approach itself and the plausibility of its results. It was concluded that the proposed method was suitable for an initial quick-scan assessment. This assessment would be valuable in the first selection of packages of measures and could support policy makers who must decide in which measures to invest, even when those measures have not yet been described or designed at a highly detailed level.

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