scholarly journals Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

2019 ◽  
Vol 11 (10) ◽  
pp. 2730 ◽  
Author(s):  
Ruone Zhang ◽  
Xin Ye ◽  
Ke Wang ◽  
Dongjin Li ◽  
Jiayu Zhu
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Low Cost ◽  
Mode Choice ◽  
Sampling Bias ◽  
Joint Model ◽  
Transit System ◽  
Explanatory Variables ◽  
Mode Choice Model ◽  
Acd Model

Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers’ active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

Large-Scale Application of a Combined Destination and Mode Choice Model Estimated with Mixed Stated and Revealed Preference Data

2017 ◽  
Vol 2669 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Michael Heilig ◽  
Nicolai Mallig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Revealed Preference ◽  
Demand Model ◽  
Agent Based ◽  
Preference Data ◽  
Mode Choice Model

The diffusion of new modes of transportation, such as carsharing and electric vehicles, makes it necessary to consider them along with traditional modes in travel demand modeling. However, there are two main challenges for transportation modelers. First, the new modes’ low share of usage leads to a lack of reliable revealed preference data for model estimation. Stated preference survey data are a promising and well-established approach to close this gap. Second, the state-of-the-art model approaches are sometimes stretched to their limits in large-scale applications. This research developed a combined destination and mode choice model to consider these new modes in the agent-based travel demand model mobiTopp. Mixed revealed and stated preference data were used, and new modes (carsharing, bikesharing, and electric bicycles) were added to the mode choice set. This paper presents both challenges of the modeling process, mainly caused by large-scale application, and the results of the new combined model, which are as good as those of the former sequential model although it also takes the new modes into consideration.

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

scholarly journals Understanding Users’ Satisfaction Towards Public Transit System in India: A Case-Study of Mumbai

2021 ◽  
Vol 10 (3) ◽  
pp. 155
Author(s):  
Rahul Das
Keyword(s):  
Service Quality ◽  
Public Transport ◽  
Travel Demand ◽  
Resource Constraints ◽  
Public Transit ◽  
Sampling Bias ◽  
Relevant Information ◽  
Transit System ◽  
Transport Services ◽  
Developing Cities

In this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user’s dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users’ dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score.

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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