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Journal article
Published: 15 January 2020 in Sustainability
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Carsharing is an emerging commute mode in China, which may produce social and environmental benefits. This paper aims to develop a commute mode choice model to explore influential factors and quantify their impacts on the potential demand for carsharing in Shanghai. The sample data were obtained from a revealed preference (RP) and stated preference (SP) survey and integrated with level-of-service attributes from road and transit networks. The RP survey collected commuters’ trip information and socioeconomic and demographic characteristics. In the SP survey, four hypothetical scenarios were designed based on carsharing’s unit price to collect commuters’ willingness to shift to carsharing. Data fusion method was applied to fuse RP and SP models. The joint model identified the target group of choosing carsharing with certain socioeconomic and demographic attributes, such as gender, age, income, household member, household vehicle ownership, and so on. It also indicates that the value of time (VOT) for carsharing is 35.56 RMB Yuan (5.08 US Dollar)/h. The elasticity and marginal effect analysis show that the direct elasticity of carsharing’s fare on its potential demand is −0.660, while the commuters, who have a more urgent plan on car purchase or are more familiar with the carsharing service, have much higher probabilities to choose carsharing as their commute modes. The developed model is expected to be applied to the urban travel demand model, providing references for the formulation of carsharing operation scheme and government policy.

ACS Style

Qian Duan; Xin Ye; Jian Li; Ke Wang. Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China. Sustainability 2020, 12, 620 .

AMA Style

Qian Duan, Xin Ye, Jian Li, Ke Wang. Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China. Sustainability. 2020; 12 (2):620.

Chicago/Turabian Style

Qian Duan; Xin Ye; Jian Li; Ke Wang. 2020. "Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China." Sustainability 12, no. 2: 620.

Article
Published: 06 January 2020 in Transportation
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This paper develops alternative stochastic frontier models (ASFM) for estimating time-space prism vertices with different distributional assumptions for the inefficiency term that takes a non-negative value. The traditional stochastic frontier model (SFM) assumes that the inefficiency term follows a half-normal or exponential distribution. Under those assumptions, most travelers’ home departure/arrival time will be close to prism vertices, which is not necessarily consistent with actual travel behaviors. To avoid this potential problem, the ASFM adopt alternative distributions for the inefficiency term whose density values can decrease monotonously or vary non-monotonously. Quasi-Monte Carlo simulation method is employed to estimate the ASFM without closed-form likelihood expressions. Simulation experiment results show that SFM needs a substantially greater number of Halton draws for consistent estimators than a typical mixed logit model does. The ASFM are estimated based on the travel data of 1454 Shanghai commuters and 2964 Houston commuters. It is found that models with inefficiency term following a half-normal distribution tend to underestimate the origin vertex of morning prism and overestimate the terminal vertex of evening prism over 50 and 30 min for Shanghai and Houston samples, respectively. The empirical results show the importance of choosing an appropriate distributional assumption for the inefficiency term in the SFM for better understanding the relation between individuals’ departure/arrival time and time-space prism vertices. The SFM based on an appropriate distributional assumption can be applied in activity-based models for big cities to better reflect tighter temporal constraints on metropolitan residents and narrower time-space prisms for outdoor activity arrangement.

ACS Style

Ke Wang; Xin Ye. Development of alternative stochastic frontier models for estimating time-space prism vertices. Transportation 2020, 1 -35.

AMA Style

Ke Wang, Xin Ye. Development of alternative stochastic frontier models for estimating time-space prism vertices. Transportation. 2020; ():1-35.

Chicago/Turabian Style

Ke Wang; Xin Ye. 2020. "Development of alternative stochastic frontier models for estimating time-space prism vertices." Transportation , no. : 1-35.

Review article
Published: 25 October 2019 in Journal of the Indian Institute of Science
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Household vehicle-ownership model is a critical part of urban transportation modeling system. This paper offers a comprehensive review on household vehicle demand models at disaggregate level, which consists of four aspects: data, methodology, application and prospect. The first section makes a relevant review on data source and type, and introduces the application of panel data and RP/SP data. In the methodology section, various modeling approaches for vehicle ownership are summarized into two broad categories, including static and dynamic models. Based on research objectives, vehicle-ownership models can be applied to forecast household vehicle count, vehicle type, vehicle use and vehicle transaction. Furthermore, the explanatory factors used in models are listed, and model applications are reviewed for emerging economies and particularly in the context of developing countries. Lastly, the prospect on the challenges and opportunities are discussed in the final section to provide references for future research.

ACS Style

Jie Ma; Xin Ye. Modeling Household Vehicle Ownership in Emerging Economies. Journal of the Indian Institute of Science 2019, 99, 647 -671.

AMA Style

Jie Ma, Xin Ye. Modeling Household Vehicle Ownership in Emerging Economies. Journal of the Indian Institute of Science. 2019; 99 (4):647-671.

Chicago/Turabian Style

Jie Ma; Xin Ye. 2019. "Modeling Household Vehicle Ownership in Emerging Economies." Journal of the Indian Institute of Science 99, no. 4: 647-671.

Journal article
Published: 17 October 2019 in Sustainability
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This paper investigates the outdoor non-work activity allocation behaviors of commuters in Xiaoshan District of Hangzhou, China, as well as the underlying relationship among different types of outdoor non-work activities. As per their commute and work schedules, commuters’ outdoor non-work activities are classified into six categories and considered as binary dependent variables for modeling analysis, including from home before work, on commute way from home to work, going home during work, going out (not going home) during work, on commute way from work back home, and from home after work. Independent variables include commute attributes, work schedules, sociodemographic attributes, and built-environmental attributes. A multivariate probit model is developed to explore the effects of explanatory variables and capture correlations among unobserved influential factors. The model estimation results show that daily work time, education years, and traffic zone have substantial impacts on commuters’ non-work activity allocations. As for the underlying relationship among unobserved factors, a positive correlation is found between the outdoor non-work activities on commute way to and from work, indicating a mutually promotive relationship. All other correlations are negative, indicating other types of non-work activities are mutually substitutive. These findings will help to better understand commuters’ behaviors of outdoor activity arrangement subject to the time-space constraint from fixed work schedules, and shed some light on the mechanism of complex work tour formation, so as to guide the development of activity-based travel demand models for commuters.

ACS Style

Xin Guan; Xin Ye; Cheng Shi; Yajie Zou. A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China. Sustainability 2019, 11, 5768 .

AMA Style

Xin Guan, Xin Ye, Cheng Shi, Yajie Zou. A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China. Sustainability. 2019; 11 (20):5768.

Chicago/Turabian Style

Xin Guan; Xin Ye; Cheng Shi; Yajie Zou. 2019. "A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China." Sustainability 11, no. 20: 5768.

Journal article
Published: 14 May 2019 in Sustainability
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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.

ACS Style

Ruone Zhang; Xin Ye; Ke Wang; Dongjin Li; Jiayu Zhu. Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data. Sustainability 2019, 11, 2730 .

AMA Style

Ruone Zhang, Xin Ye, Ke Wang, Dongjin Li, Jiayu Zhu. Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data. Sustainability. 2019; 11 (10):2730.

Chicago/Turabian Style

Ruone Zhang; Xin Ye; Ke Wang; Dongjin Li; Jiayu Zhu. 2019. "Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data." Sustainability 11, no. 10: 2730.

Journal article
Published: 15 January 2019 in Sustainability
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Wildlife‒vehicle collision (WVC) data usually contain two types: the reported WVC data and carcass removal data. Previous studies often found a discrepancy between the number of reported WVC and carcass removal data, and the quality of both datasets is affected by underreporting. Underreporting means the number of WVCs is not fully recorded in the database; neglecting the underreporting in WVC data may result in biased parameter estimation results. In this study, a copula regression model linking wildlife‒vehicle collisions and the underreporting outcome was proposed to consider the underreporting in WVC data. The WVC data collected from 10 highways in Washington State were analyzed using the copula regression model and the Negative Binomial (NB) model. The main findings from this study are as follows: (1) the Gaussian copula model can provide different modeling results when compared with the conventional modeling approach; (2) the hotspot identification results indicate that the Gaussian copula-based Empirical Bayes (EB) method can more accurately identify hotspots than the NB-based EB method. Thus, the proposed copula model may be a better alternative to the conventional NB model for modeling underreported WVC data.

ACS Style

Yajie Zou; Xinzhi Zhong; Jinjun Tang; Xin Ye; Lingtao Wu; Muhammad Ijaz; Yinhai Wang. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability 2019, 11, 418 .

AMA Style

Yajie Zou, Xinzhi Zhong, Jinjun Tang, Xin Ye, Lingtao Wu, Muhammad Ijaz, Yinhai Wang. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability. 2019; 11 (2):418.

Chicago/Turabian Style

Yajie Zou; Xinzhi Zhong; Jinjun Tang; Xin Ye; Lingtao Wu; Muhammad Ijaz; Yinhai Wang. 2019. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis." Sustainability 11, no. 2: 418.

Journal article
Published: 12 October 2018 in Sustainability
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With the rapid increase of motorization in China, transitions have taken place in regards to traditional private transportation modes. This paper aims to understand four types of vehicle ownership within a household, including automobile, motorcycle, electric bicycle and human-powered bicycle. This study presents a cross-sectional multivariate ordered probit model, with a composite marginal likelihood estimation approach that accommodates the effects of explanatory variables, and capturing the dependence among the propensity to household vehicle ownership. The sample data are obtained from the residents’ household travel survey of Xiaoshan District, Hangzhou, in 2015, which can analyze the significant effects of sociodemographic attributes and built environment attributes. Interestingly, the major findings suggest that: (1) The households with higher income tend to own more automobiles, yet the effect is not obvious with a small value of elasticity, which is similar to developed countries. (2) The household education level, which takes a positive effect on automobile ownership, is a more elastic factor than income. (3) The higher population density contributes to less ownership of automobiles and motorcycles, due to traffic congestions and parking challenges. (4) There is a large substitutive relation between automobile and electric bicycle/motorcycle, and the vehicle ownership of electric bicycle/motorcycle and bicycle are mutually promoted, while motorcycle and electric-bicycle are mutually substituted.

ACS Style

Jie Ma; Xin Ye; Cheng Shi. Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China. Sustainability 2018, 10, 3660 .

AMA Style

Jie Ma, Xin Ye, Cheng Shi. Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China. Sustainability. 2018; 10 (10):3660.

Chicago/Turabian Style

Jie Ma; Xin Ye; Cheng Shi. 2018. "Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China." Sustainability 10, no. 10: 3660.

Journal article
Published: 03 July 2018 in Transportation Research Part C: Emerging Technologies
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In this paper, a destination choice model with pairwise district-level constants is proposed for trip distribution based on a nearly complete OD trip matrix in a region. It is found that the coefficients are weakly identified in a destination choice model with pairwise zone-level constants. Thus, a destination choice model with pairwise district-level constants is then proposed and an iterative algorithm is developed for model estimation. Herein, the “district” means a spatial aggregation of a number of zones. The proposed model is demonstrated through simulation experiments. Then, destination choice models with and without pairwise district-level constants are estimated based on GPS data of taxi passenger trips collected during morning peak hours within the Inner Ring Road of Shanghai, China. The datasets comprise 504,187 trip records and a sample of 10,000 taxi trips for model development. The zones used in the study are actually 961 residents’ committees while the districts are 52 residential districts that are spatial aggregations and upper-level administrative units of residents’ committees. It is found that the estimated value of time dramatically drops after the involvement of district-level constants, indicating that the traditional model tends to overestimate the value of time when ignoring pairwise associations between two zones in trip distribution. The proposed destination choice model can ensure its predicted trip OD matrix to match the observed one at district level. Thus, the proposed model has potential to be widely applied for trip distribution under the situation where a complete OD trip matrix can be observed.

ACS Style

Jiayu Zhu; Xin Ye. Development of destination choice model with pairwise district-level constants using taxi GPS data. Transportation Research Part C: Emerging Technologies 2018, 93, 410 -424.

AMA Style

Jiayu Zhu, Xin Ye. Development of destination choice model with pairwise district-level constants using taxi GPS data. Transportation Research Part C: Emerging Technologies. 2018; 93 ():410-424.

Chicago/Turabian Style

Jiayu Zhu; Xin Ye. 2018. "Development of destination choice model with pairwise district-level constants using taxi GPS data." Transportation Research Part C: Emerging Technologies 93, no. : 410-424.

Conference paper
Published: 18 January 2018 in CICTP 2017
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In the developing country of China, driving used to be considered a travel mode that symbolized identity. However, with the explosive growth of auto ownership, traffic congestion and parking challenges have become increasingly serious in large cities. Some auto owners have switched to urban rail transit for commuting. The reasons behind this shift are worthy of in-depth studies. In this paper, a binary logit model is developed for auto owners in Shanghai, China, to identify influential factors and quantify their impacts on probabilities of choices between auto and rail for commute. The model involves a variety of explanatory variables including rail travel time, station access/egress distance, rail fare, rail waiting time, in-car time as well as some commuters’ socioeconomic and demographic characteristics. The research findings can aid in proposing programs to improve other existing transit facilities and providing a basis for quantitative assessment of alternative improvement programs.

ACS Style

Ruone Zhang; Ke Wang; Jiayu Zhu; Xin Ye. Development of Mode Choice Model to Understand Why Auto Owners Use Rail for Commute in Shanghai, China. CICTP 2017 2018, 1 .

AMA Style

Ruone Zhang, Ke Wang, Jiayu Zhu, Xin Ye. Development of Mode Choice Model to Understand Why Auto Owners Use Rail for Commute in Shanghai, China. CICTP 2017. 2018; ():1.

Chicago/Turabian Style

Ruone Zhang; Ke Wang; Jiayu Zhu; Xin Ye. 2018. "Development of Mode Choice Model to Understand Why Auto Owners Use Rail for Commute in Shanghai, China." CICTP 2017 , no. : 1.

Conference paper
Published: 18 January 2018 in CICTP 2017
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In this paper, the multiple discrete-continuous extreme value (MDCEV) modeling framework is employed to model daily time allocation behavior in Xiaoshan District in the City of Hangzhou, China. The data for model development are collected from a household survey recently conducted in its urban area based on an advanced survey system using portable tablet computer with web-based map service. The models are developed to identify the constraint from mandatory work schedules. In the worker model, it is found that the total work time, work schedule, commuting time, commuting travel mode significantly affect daily time allocations on other non-mandatory activities. Other impact factors include worker's age, gender, education level, household income, and population density of residential area. In the non-worker model, influential factors include non-workers' age, gender, household size, household income, permanent residency, and population density of residential area. It is found that shifting the work schedule one hour earlier from 8:00 - 17:00 to 7:00 - 16:00 will allow workers to allocate extra about 40 minutes on out-of-home activities but cut the same amount of time on in-home activities. Lengthening (or shortening) daily work time by 1 hour will cut (or add) about 45 minutes on in-home activities and about 15 minutes on out-of-home activities.

ACS Style

Cheng Shi; Haixiao Pan; Ying Hui; Xin Ye. Daily Time Allocation Behavior Analysis in Xiaoshan District of Hangzhou, China. CICTP 2017 2018, 1 .

AMA Style

Cheng Shi, Haixiao Pan, Ying Hui, Xin Ye. Daily Time Allocation Behavior Analysis in Xiaoshan District of Hangzhou, China. CICTP 2017. 2018; ():1.

Chicago/Turabian Style

Cheng Shi; Haixiao Pan; Ying Hui; Xin Ye. 2018. "Daily Time Allocation Behavior Analysis in Xiaoshan District of Hangzhou, China." CICTP 2017 , no. : 1.

Conference paper
Published: 18 January 2018 in CICTP 2017
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In this paper, a destination choice model is developed for taxi passengers based on taxi GPS data from Shanghai, China. Taxi GPS data belong to passively collected big data that can avoid possible biases in traditional travel surveys limited by the sampling process, and potential discrepancies between respondents’ actual behaviors and their responses. As a discrete choice model, a destination choice model can involve policy-sensitive variables in a flexible way, so as to predict and evaluate policy impacts. The developed model incorporates a variety of explanatory variables, including travel impedance variables (travel time and monetary cost), location indicator variables (whether an airport or passenger railway station is in the traffic analysis zone, i.e. TAZ) and attraction variables in trip destination ends (population and employment). Finally, the factors influencing taxi passengers’ destination choice behaviors are analyzed based on the model estimation results.

ACS Style

Jiayu Zhu; Xin Ye. Development of Destination Choice Model for Taxi Passengers in Shanghai, China. CICTP 2017 2018, 1 .

AMA Style

Jiayu Zhu, Xin Ye. Development of Destination Choice Model for Taxi Passengers in Shanghai, China. CICTP 2017. 2018; ():1.

Chicago/Turabian Style

Jiayu Zhu; Xin Ye. 2018. "Development of Destination Choice Model for Taxi Passengers in Shanghai, China." CICTP 2017 , no. : 1.

Research article
Published: 26 October 2017 in PLOS ONE
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A semi-nonparametric generalized multinomial logit model, formulated using orthonormal Legendre polynomials to extend the standard Gumbel distribution, is presented in this paper. The resulting semi-nonparametric function can represent a probability density function for a large family of multimodal distributions. The model has a closed-form log-likelihood function that facilitates model estimation. The proposed method is applied to model commute mode choice among four alternatives (auto, transit, bicycle and walk) using travel behavior data from Argau, Switzerland. Comparisons between the multinomial logit model and the proposed semi-nonparametric model show that violations of the standard Gumbel distribution assumption lead to considerable inconsistency in parameter estimates and model inferences.

ACS Style

Ke Wang; Xin Ye; Ram M. Pendyala; Yajie Zou. On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices. PLOS ONE 2017, 12, e0186689 .

AMA Style

Ke Wang, Xin Ye, Ram M. Pendyala, Yajie Zou. On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices. PLOS ONE. 2017; 12 (10):e0186689.

Chicago/Turabian Style

Ke Wang; Xin Ye; Ram M. Pendyala; Yajie Zou. 2017. "On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices." PLOS ONE 12, no. 10: e0186689.

Research article
Published: 19 July 2016 in Mathematical Problems in Engineering
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Joint destination-mode travel choice models are developed for intercity long-distance travel among sixteen cities in Yangtze River Delta Megaregion of China. The model is developed for all the trips in the sample and also by two different trip purposes, work-related business and personal business trips, to accommodate different time values and attraction factors. A nested logit modeling framework is applied to model trip destination and mode choices in two different levels, where the lower level is a mode choice model and the upper level is a destination choice model. The utility values from various travel modes in the lower level are summarized into a composite utility, which is then specified into the destination choice model as an intercity impedance factor. The model is then applied to predict the change in passenger number from Shanghai to Yangzhou between scenarios with and without high-speed rail service to demonstrate the applicability. It is helpful for understanding and modeling megaregional travel destination and mode choice behaviors in the context of developing country.1. IntroductionA megaregion (also known as megalopolis or megapolitan city) can be defined as a chain of roughly adjacent metropolitan areas with strong social and economic linkages [1]. Megaregions are rising around the world. America 2050 [2], a program of the Regional Plan Association, has identified eleven megaregions in North America. In Europe, except for some well-known megaregions within a country (e.g., London metropolitan area, Paris metropolitan area), there are even some transnational megaregions, like Blue Banana, being formed across countries [3–5]. There are also several megaregions being developed in other continents, such as Sydney Region in Australia, Tokyo urban agglomeration in Asia, the megacity of Cairo in Africa, and the megacity of Sao Paulo in South America [6].With rapid urbanization in China, ten major megaregions (also called urban agglomeration) are being formed in China according to the research supported by the National Development and Reform Commission [7, 8]. Those regions cover less than 10% of the land but accommodate more than one-third of the population and create more than a half of GDP (Gross Domestic Product) of the entire country. There are also several other megaregions being formed and developed, which will allow more Chinese people to live in urban areas and enjoy urban life.The formation and development of a megaregion largely depend on its intercity transportation system that needs to be efficient and convenient enough to support highly frequent and intense movements of passengers and freights between cities in the region. Among all those megaregions in China, Yangtze River Delta Megaregion is the largest one in terms of population (about 110 million) and economic size (annual GDP is nearly 10 trillion RMB Yuan). As per “the National Urban System Plan for 2005–2020” compiled by the Ministry of Construction in 2006, this megaregion consists of 16 cities including its central city Shanghai, the highly developed city Suzhou, Jiangsu Province’ capital Nanjing, and Zhejiang Province’ capital Hangzhou (see Section 4, Figure 1 and Table 1 for detailed information). Among many of those cities, there are not only interconnected expressways but also high-speed rails that can operate trains at a speed of 180 mph. For example, it takes only 1 hour in a high-speed train to travel between Shanghai and Hangzhou, which are more than 100 miles away from one to the other. Such a multimodal intercity transportation system can greatly strengthen connections among cities and thereby improve their mutual collaborations and enhance competitiveness of the entire megaregion.Table 1: Characteristics of 16 cities in Yangtze River Delta Megaregion.Figure 1: Sixteen cities of Yangtze River Delta Megaregion.It is highly desired to choose this megaregion as a typical example to investigate how a multimodal intercity transportation system aids in forming and developing a megaregion by strengthening connections among its cities. It is critical to understand the joint destination and mode choice behaviors of intercity travelers within the entire megaregion. This kind of study can provide valuable references for future transportation planning and development in China and the rest of the world, where new megaregions are gradually being formed and developed.The remainder of the paper is organized as below. Relevant literature will be first reviewed in Section 2, while the modeling methodology will be detailed in Section 3. Section 4 will introduce the procedure to collect all kinds of data for model development and provide descriptions for the collected data. Model estimation and simulation results will be discussed in Sections 5 and 6, respectively. Finally, conclusions and discussions will be made in Section 7.2. Literature ReviewMany megaregional areas appear with the development of transportation system that closely connects adjacent cities. In this context, intercity passenger travels are of more concern for researchers. The research on passengers’ intercity travel is mainly focused on travel mode choice and destination choice models.Some researchers conducted travel survey to observe the characteristic of passengers’ intercity travels and their preferences on travel choices [9, 10]. Some studies show that trip purpose affects mode choice [11], and market segmentation is important for model development [12, 13]. Thus, intercity mode choice models are usually developed by trip purposes. In the literature, two trip purposes, business and tourism, are usually differentiated for mode choice model development. Dong et al. developed a fractional multinomial logit model to analyze mode choice of intercity business trips in Yangtze River Megaregion [14, 15]. Manssour et al. developed a binary logit model for mode choice of intercity business trips in Libya [16]. It is also found that the socioeconomic attributes and land use are significant factors to influence mode choice [11, 17, 18]. Thrane examined tourists’ long-distance travel mode choices by developing a multinomial logit model [18]. Cohen and Harris analyzed mode choice of trips visiting friends and relatives [19].Meanwhile, the research on intercity trip destination choice is mainly conducted for tourists’ intercity travel, where mode choice is also considered in a destination choice model [20]. Destination choice models are also developed for intracity trips. When a destination choice model is built up for intracity trips, mode choice is usually also incorporated as an important explanatory variable [21, 22].As Koppelman indicated in 1989, the intercity travel decisions are interrelated and cannot be dealt with separately; a joint model is therefore needed to better address interrelations among multiple travel decisions [23]. Actually, a joint model for travel destination and mode choice is not a new modeling technique. As early as in 1981, Southworth developed such a joint model for interrelated mode and destination choices [24]. Afterwards, many joint models are developed for travels within an urban area [25, 26]. However, few studies are found to develop a joint mode-destination choice model for a megaregional area possibly due to the lack of survey data on megaregional intercity long-distance travels. Yao and Morikawa developed a nested structure of integrated intercity travel demand model for induced demand and applied the model to evaluate intercity transport projects in Japan [27]. The intercity trips were classified into business and nonbusiness trips. Their study is focused on an intercity corridor along six megaregions, rather than inside a megaregion (the mean distance of trips in the Revealed Preference and Stated Preference surveys is larger than 300 km, including air trips). However, most intercity trips in a megaregion are less than 300 km and therefore travel modes generally do not include airplane. Thus, there is a lack of papers in the literature on the topic of joint destination-mode choice model for intercity trips within a megaregion.Besides, most existing studies are conducted in the context of developed countries but it is rare to see similar work in the context of a developing country like China. In fact, due to economic and cultural differences between developing and developed countries, megaregional travel mode choice behaviors are quite different. Take US as an example, the market share of auto mode is dominant but in this megaregion of China the rail and bus modes take more than 40%. In particular, the development of high-speed rail plays an important role in forming megaregions in a developing country like China by facilitating the intercity transportation and collaboration. Thus, it is necessary to develop an empirical model especially for the megaregion in a developing country.To fill this gap, the authors make an attempt to develop such a joint destination and mode choice model for intercity trips based on an intercept travel survey in Yangtze River Delta Megaregion. In this paper, the joint destination-mode choice model is developed by two purposes, including work-related business and personal business, with consideration of available sample size and experience from the literature. And with the economic development and travel demand diversity in the developing country, some new attraction variables are used to measure the attractiveness of city for intercity trips within a megaregion. It is also informative to apply the joint model into practice to evaluate how the investment on multimodal transportation system helps to build up a strong connection between cities in a megaregion. For practice, the authors also estimate a practical model without personal characteristics for all the trips in the sample. In a scenario analysis, the practical model is applied to predict the change in daily passenger number from Shanghai to Yangzhou under

ACS Style

Yanli Wang; Bing Wu; Zhi Dong; Xin Ye. A Joint Modeling Analysis of Passengers’ Intercity Travel Destination and Mode Choices in Yangtze River Delta Megaregion of China. Mathematical Problems in Engineering 2016, 2016, 1 -10.

AMA Style

Yanli Wang, Bing Wu, Zhi Dong, Xin Ye. A Joint Modeling Analysis of Passengers’ Intercity Travel Destination and Mode Choices in Yangtze River Delta Megaregion of China. Mathematical Problems in Engineering. 2016; 2016 ():1-10.

Chicago/Turabian Style

Yanli Wang; Bing Wu; Zhi Dong; Xin Ye. 2016. "A Joint Modeling Analysis of Passengers’ Intercity Travel Destination and Mode Choices in Yangtze River Delta Megaregion of China." Mathematical Problems in Engineering 2016, no. : 1-10.

Journal article
Published: 12 March 2016 in KSCE Journal of Civil Engineering
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This paper aims to evaluate and compare impacts of two alternative Transit-Oriented Development (TOD) policies, concentrating growth of population or employment opportunities in transit service area, on travel demand measures of mode share, trip distance and highway usage. A validated Maryland Statewide Transportation Model (MSTM) is employed to forecast changes in travel demand measures under various TOD policy scenarios in a future year of 2030. The model simulation results show either concentrating population or employment policy has similar impacts on raising transit mode share and reducing auto mode share. However, concentrating population policy decreases average trip distance while concentrating employment policy increases it. Consequently, concentrating population policy reduces highway usage, measured by Vehicle Miles Traveled (VMT), more effectively than concentrating employment policy in this specific region given the existing land use pattern. The findings in this paper have important implications to urban planners, transportation planners and decision makers in Maryland of US. The paper also provides a good example for applying a travel demand model to evaluate and compare alternative TOD policies based on travel demand measures.

ACS Style

Yanli Wang; Timothy F. Welch; Bing Wu; Xin Ye; Frederick W. Ducca. Impact of transit-oriented development policy scenarios on travel demand measures of mode share, trip distance and highway usage in Maryland. KSCE Journal of Civil Engineering 2016, 20, 1006 -1016.

AMA Style

Yanli Wang, Timothy F. Welch, Bing Wu, Xin Ye, Frederick W. Ducca. Impact of transit-oriented development policy scenarios on travel demand measures of mode share, trip distance and highway usage in Maryland. KSCE Journal of Civil Engineering. 2016; 20 (3):1006-1016.

Chicago/Turabian Style

Yanli Wang; Timothy F. Welch; Bing Wu; Xin Ye; Frederick W. Ducca. 2016. "Impact of transit-oriented development policy scenarios on travel demand measures of mode share, trip distance and highway usage in Maryland." KSCE Journal of Civil Engineering 20, no. 3: 1006-1016.