A heuristic-based population synthesis method for micro-simulation in transportation

Zhuge, Chengxiang; Li, Xia; Ku, Chia-An; Gao, Jian; Zhang, Hui

doi:10.1007/s12205-016-0704-1

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…In order to take into account both heterogeneity and spatial factors (e.g., neighbour effect), the EV market model needs to be coupled with a population synthesizer (Pritchard and Miller, 2012), a social network generator (Arentze et al, 2012), and an activity-based travel demand model (Horni et al, 2016;Zhuge et al, 2017). Specifically, population synthesizer is used to generate a synthetic population containing individuals and households, as well as their attributes (e.g., income and car ownership) (Pritchard and Miller, 2012;Zhuge et al, 2016a;, which can be used as the inputs of the MNL models to predict the weights of each factor; the social network generator is used to generate a population-wide social network (Arentze et al, 2012;, so that the three types of social influence can be quantified and the results can be further used as the inputs of the utility function; Activity-based travel demand model, which is used to simulate the daily travel of each individual in the population (Horni et al, M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2016), can be used to quantify the vehicle usage and environmental awareness (e.g., the total amount of vehicular emissions) by aggregating the micro-simulation results. In addition, this paper used four general clusters, namely "Very High", "High", "Medium" and "Low", to group the weights of each factor, using a K-means clustering algorithm with K set to 4.…”

Section: Discussionmentioning

confidence: 99%

Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives

Zhuge

Shao

2019

Journal of Cleaner Production

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Electrifying urban transportation through the adoption of Electric Vehicles (EVs) has great potential to mitigate two global challenges, namely climate change and energy scarcity, and also to improve local air quality and further benefit human health. This paper was focused on the six typical factors potentially influencing the purchase behaviour of EVs in Beijing, China, namely vehicle price, vehicle usage, social influence, environmental awareness, purchase-related policies and usage-related policies. Specifically, this study used the data collected in a paper-based questionnaire survey in Beijing from September, 2015 to March, 2016, covering all of the 16 administrative regions, and tried to quantify the relative importance of the six factors, based on their weights (scores) given by participants. Furthermore, Multinomial Logit (MNL) models and Moran's I (a measure of global spatial autocorrelation) were used to analyse the weights of each factor from statistical and spatial perspectives, respectively. The results suggest that 1) vehicle price and usage tend to be more influential among the six factors, accounting for 32.3% and 28.1% of the importance; 2) Apart from the weight of social influence, the weights of the other five factors are closely associated with sociodemographic characteristics, such as individual income and the level of education; 3) people having similar attitudes towards vehicle usage (Moran's I= 0.10) and purchase restriction (Moran's I= 0.14) tend to live close to each other. This paper concludes with a discussion on applying the empirical findings in policy making and modelling of EV purchase behaviour.

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Section: Discussionmentioning

confidence: 99%

Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives

Zhuge

Shao

2019

Journal of Cleaner Production

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“…(Farooq, Bierlaire, Hurtubia, & Flötteröd, 2013) proposed a Markov Chain Monte Carlo (MCMC) simulation approach for population synthesis to overcome the shortcomings in fitting-based procedures, such as an over reliance on the accuracy of the sample contingency tables and losing population heterogeneity. (Zhuge, Li, Ku, Gao, & Zhang, 2017) proposed a heuristic-based population synthesis method for transportation related applications as a counter view to the optimization notion associated with conventional population synthesis approaches. In fitting-based approaches the objective is to minimize the mean absolute percentage error of control variables, while (Zhuge et al, 2017) argues that the standard deviation of control variables is also crucial in some cases.…”

Section: Transportation Systemmentioning

confidence: 99%

“…(Zhuge, Li, Ku, Gao, & Zhang, 2017) proposed a heuristic-based population synthesis method for transportation related applications as a counter view to the optimization notion associated with conventional population synthesis approaches. In fitting-based approaches the objective is to minimize the mean absolute percentage error of control variables, while (Zhuge et al, 2017) argues that the standard deviation of control variables is also crucial in some cases. They implemented their method in the medium-size city of Baoding in China.…”

Section: Transportation Systemmentioning

confidence: 99%

An agent-based transportation impact sketch planning (TISP) model system

Hammadi

Miller

2021

JTLU

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A traffic impact sketch planning (TISP) model is presented for the estimation of the likely travel demand generated by a major land-use development or redevelopment project. The proposed approach overcomes the problems with the non-behavioral transportation-related studies used in practice for assessing the development design impacts on the local transportation system. The architectural design of the development, in terms of the number and type of dwellings, by number of bedrooms per unit, and the land-use categories of the non-residential floorspace, are reflected in the TISP model through an integrated population and employment synthesis approach. The population synthesis enables the feasible deployment of an agent-based microsimulation (ABM) model system of daily activity and travel demand for a quick, efficient, and detailed assessment of the transportation impacts of a proposed neighborhood or development. The approach is not restricted to a certain type of dataset of the control variables for the geographic location of the development. Datasets for different geographic dimensions of the study area, with some common control variables, are merged and cascaded into a synthesized, disaggregate population of resident persons, households and jobs. The prototype implementation of the TISP model is for Waterfront Toronto’s Bayside Development Phase 2, using the operational TASHA-based GTAModel V4.1 ABM travel demand model system. While the conventional transportation studies focus on the assessment of the local traffic impacts in the immediate surroundings of the development, the TISP model investigates and assesses many transportation related impacts in the district, city, and region, for both residents and non-residents of the development. TISP model analysis includes the overall spatiotemporal trips distribution generated by the residents and non-residents of the development for the auto and non-auto mobility systems and the simulated agents diurnal peaking travel times. The model results are compared with the trips estimates by a prior project traffic impact study and the Institute of Transportation Engineers (ITE) Trip Generation Manual (TGM) rates of weekday trips for the relevant land uses. Future extensions and improvements of the model including the generalization and full automation of the model, and the bi-level macro-micro representation of the transportation network are also discussed.

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“…The heuristic-based approach was developed by Zhuge et al [30] to address two IPF limitations that received less attention from earlier studies. The first limitation stems from the existence of various solutions for one target marginal distribution.…”

Section: Emerging Approachesmentioning

confidence: 99%

A Critical Review on Population Synthesis for Activity- and Agent-Based Transportation Models

Ramadan¹,

Sisiopiku²

2020

Transportation Systems Analysis and Assessment

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Traditional four-step transportation planning models fail to capture novel transportation modes such as car/ridesharing. Hence, agent-based models are replacing those traditional models for their scalability, robustness, and capability of simulating nontraditional transportation modes. A crucial step in developing agent-based models is the definition of agents, e.g., household and persons. While model developers wish to capture typical workday travel patterns of the entire study population of travelers, such detailed data are unavailable due to privacy concerns and technical and financial feasibility issues. Hence, modelers opt for population syntheses based on travel diary surveys, land use data, and census data. The most prominent techniques are iterative proportional fitting (IPF), iterative proportional updating (IPU), combinatorial optimization, Markov-based and fitness-based syntheses, and other emerging approaches. Yet, at present, there is no clear guideline on using any of the available techniques. To bridge this gap, this chapter presents a comprehensive synthesis of practice and documents available successful studies.

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A heuristic-based population synthesis method for micro-simulation in transportation

Cited by 6 publications

References 18 publications

Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives

Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives

An agent-based transportation impact sketch planning (TISP) model system

A Critical Review on Population Synthesis for Activity- and Agent-Based Transportation Models

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