Estimating a mode choice model with incomplete data

1989 ◽  
Vol 16 (6) ◽  
pp. 917-923
Author(s):  
Gerald Brown ◽  
Siovache Kahkeshan
Keyword(s):  
Survey Data ◽  
Incomplete Data ◽  
Choice Model ◽  
Mode Choice ◽  
Synthetic Data ◽  
Level Of Service ◽  
Data Simulation ◽  
Data Set ◽  
Policy Model ◽  
Mode Choice Model

This study develops and uses a synthetic data base to calibrate a logit mode choice model of work trips in metropolitan Vancouver. Missing survey data entries for perceived measures of travel time and waiting time by bus, as well as operating and parking cost by car, are calculated using statistical methods to increase the survey sample of 275 complete cases to 621 usable cases. The synthesized data set is used to specify random utility functions for two planning assumptions. The short-term policy specification using only level of service variables does not produce a usable model, but the specification based on a long-term planning assumption using a combination of level of service and socioeconomic variables produces plausible results. The inconclusive results from the policy model could be due to survey data problems, data simulation, and (or) the lack of conceptual validity of perceived measures of transportation attributes. The planning model provides insight into mode split prediction and transportation management for cities that are undergoing dynamic demographic and social changes. Key words: mode choice, incomplete data, socioeconomic factors, logit model.

scholarly journals A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model

2021 ◽  
Vol 1 (6) ◽  
Author(s):  
Alperen Bektas ◽  
Valentino Piana ◽  
René Schuman
Keyword(s):  
Choice Model ◽  
Mode Choice ◽  
Statistical Distribution ◽  
Empirical Validation ◽  
Behavioral Patterns ◽  
Micro Data ◽  
Data Set ◽  
Agent Based ◽  
Meso Level ◽  
Mode Choice Model

AbstractThe complex nature of agent-based modeling may reveal more descriptive accuracy than analytical tractability. That leads to an additional layer of methodological issues regarding empirical validation, which is an ongoing challenge. This paper offers a replicable method to empirically validate agent-based models, a specific indicator of “goodness-of-validation” and its statistical distribution, leading to a statistical test in some way comparable to the p value. The method involves an unsupervised machine learning algorithm hinging on cluster analysis. It clusters the ex-post behavior of real and artificial individuals to create meso-level behavioral patterns. By comparing the balanced composition of real and artificial agents among clusters, it produces a validation score in [0, 1] which can be judged thanks to its statistical distribution. In synthesis, it is argued that an agent-based model can be initialized at the micro-level, calibrated at the macro-level, and validated at the meso-level with the same data set. As a case study, we build and use a mobility mode-choice model by configuring an agent-based simulation platform called BedDeM. We cluster the choice behavior of real and artificial individuals with the same ex-ante given characteristics. We analyze these clusters’ similarity to understand whether the model-generated data contain observationally equivalent behavioral patterns as the real data. The model is validated with a specific score of 0.27, which is better than about 95% of all possible scores that the indicator can produce. By drawing lessons from this example, we provide advice for researchers to validate their models if they have access to micro-data.

scholarly journals Exploring Travel Mode Preference of External Trips for Smart City Transportation Planning: Sejong, Korea

2022 ◽  
Vol 14 (2) ◽  
pp. 630
Author(s):  
Jin-Ki Eom ◽  
Kwang-Sub Lee ◽  
Sangpil Ko ◽  
Jun Lee
Keyword(s):  
Survey Data ◽  
Smart City ◽  
Choice Model ◽  
Smart Cities ◽  
Mode Choice ◽  
Driving Ability ◽  
Planning Stage ◽  
Major Mode ◽  
Mode Choice Model ◽  
Living Area

In the face of growing concerns about urban problems, smart cities have emerged as a promising solution to address the challenges, for future sustainable societies in cities. Since the early 2000s, 67 local governments in Korea have been participating in smart city projects, as of 2019. The Sejong 5-1 Living Area smart city was selected as one of two pilot national demonstration smart cities. The main objectives of this study are to introduce the Sejong 5-1 Living Area smart city project that is currently in the planning stage, present travel and mode preferences focusing on external trips in a smart city context to be built, and analyze a mode choice model according to the socioeconomic characteristics of individual travelers. One of the distinguishing features of the Sejong smart city is its transportation design concept of designating a sharing car-only district within the city to limit private vehicle ownership to about one-third of residents, while bus rapid transit (BRT) plays a central role in mobility for external trips among four transport modes including private cars, BRT, carsharing, and ridesharing. This study was analyzed using the stated preference survey data under hypothetical conditions by reflecting the unique characteristics of the Sejong smart city transportation policy. Approximately two-thirds of respondents in the survey preferred to spend less than 1.25 USD, traveling less than 35 min on BRT trips. On the basis of the survey data, we developed a mixed logit mode choice model and found the overall model estimates to be statistically significant and reasonable. All people-specific variables examined in this study were associated with mode choices for external commuting trips, including age, income, household size, major mode, driving ability, and presence of preschoolers.

scholarly journals Econometric Calibration of the Joint Time Assignment–Mode Choice Model

2008 ◽  
Vol 42 (2) ◽  
pp. 208-219 ◽  
Author(s):  
Marcela Munizaga ◽  
Sergio Jara-Díaz ◽  
Paulina Greeven ◽  
Chandra Bhat
Keyword(s):  
Choice Model ◽  
Mode Choice ◽  
Mode Choice Model

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

scholarly journals A two-stage residential location and transport mode choice model with exposure to traffic-induced air pollution

2021 ◽  
Author(s):  
Mirjam Schindler ◽  
JYT Wang ◽  
RD Connors
Keyword(s):  
Air Pollution ◽  
Choice Model ◽  
Mode Choice ◽  
Residential Location ◽  
Health Concern ◽  
Spatially Explicit ◽  
Population Exposure ◽  
Population Densities ◽  
Transport Mode ◽  
Mode Choice Model

Air pollution is an increasing concern to urban residents. In response, residents are beginning to adapt their travel behaviour and to consider local air quality when choosing a home. We study implications of such behaviour for the morphology of cities and population exposure to traffic-induced air pollution. To do so, we propose a spatially explicit and integrated residential location and transport mode choice model for a city with traffic-induced air pollution. Intra-urban spatial patterns of population densities, transport mode choices, and resulting population exposure are analysed for urban settings of varying levels of health concern and air pollution information available to residents. Numerical analysis of the feedback between residential location choice and transport mode choice, and between residents' choices and the subsequent potential impact on their own health suggests that increased availability of information on spatially variable traffic-induced health concerns shifts population towards suburban areas with availability of public transport. Thus, health benefits result from reduced population densities close to urban centres in this context. To mitigate population exposure, our work highlights the need for spatially explicit information on peoples' air pollution concerns and, on this basis, spatially differentiated integrated land use and transport measures.

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