Issues in Feathers Application in the Seoul Metropolitan Area

Author(s):  
Won Do Lee ◽  
Chang-Hyeon Joh ◽  
Sungjin Cho ◽  
Bruno Kochan

Over the last decades, the trip-based approach, also known as the four-step model, has been playing an unrivaled role in transportation demand research in Korea. It has been used to predict changes in traffic volume resulting from new transportation policy measures, and also has allowed conducting benefit-cost analyses for new infrastructure provisions. It has been increasingly difficult for the trip-based model to anticipate individual responses to new transportation policy inputs and infrastructure provision as the society becomes personalized and diversified. Activity-Based Modeling (ABM) approaches, predicting travel demand derived from individual activity participations, were introduced to complement the trip-based approach in this regard. The chapter introduces the Seoul ABM project that aims to first apply FEATHERS as an ABM to the data collected in Seoul Metropolitan Area (SMA) and then develop a prototype of the ABM framework for Korea. More specifically, the chapter first briefly describes SMA in comparison with Flanders in Belgium and other countries. It then introduces related research works in Korea and the background of the Seoul ABM project. After these, a FEATHERS framework applied for the Seoul ABM project is described with its data requirements. Major issues of and solutions to the Seoul ABM project are then discussed with regard to the data preprocessing. The chapter ends with a summary and future work.

Author(s):  
Won Do Lee ◽  
Chang-Hyeon Joh ◽  
Sungjin Cho ◽  
Bruno Kochan

Over the last decades, the trip-based approach, also known as the four-step model, has been playing an unrivaled role in transportation demand research in Korea. It has been used to predict changes in traffic volume resulting from new transportation policy measures, and also has allowed conducting benefit-cost analyses for new infrastructure provisions. It has been increasingly difficult for the trip-based model to anticipate individual responses to new transportation policy inputs and infrastructure provision as the society becomes personalized and diversified. Activity-Based Modeling (ABM) approaches, predicting travel demand derived from individual activity participations, were introduced to complement the trip-based approach in this regard. The chapter introduces the Seoul ABM project that aims to first apply FEATHERS as an ABM to the data collected in Seoul Metropolitan Area (SMA) and then develop a prototype of the ABM framework for Korea. More specifically, the chapter first briefly describes SMA in comparison with Flanders in Belgium and other countries. It then introduces related research works in Korea and the background of the Seoul ABM project. After these, a FEATHERS framework applied for the Seoul ABM project is described with its data requirements. Major issues of and solutions to the Seoul ABM project are then discussed with regard to the data preprocessing. The chapter ends with a summary and future work.


Author(s):  
T. Donna Chen ◽  
Kara Kockelman ◽  
Yong Zhao

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


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