Jinping Guan

As cities worldwide pursue sustainable and inclusive mobility, understanding the travel behavior of vulnerable populations in peri-urban resettlement areas is essential for designing equitable built environments. In Shanghai, as in many rapidly urbanizing megacities, government-led redevelopment in central Shanghai displaces residents to the urban periphery, creating accessibility deficits and raising transport inclusivity concerns. Using travel diary data from five peri-urban affordable housing communities collected in 2012 (N = 8390)—a critical baseline before major infrastructure improvements—this study documents limited mobility (residents make significantly fewer trips than the city average) and examines its correlates via logistic regression. A core finding is a type-based threshold effect: awareness of a nearby large shopping mall is associated with higher shopping trip frequency (coefficient −0.332, p = 0.024), whereas awareness of small-to-medium supermarkets shows no significant association. This pattern is consistent with elastic demand theory and challenges the assumption that any retail facility improves accessibility equally. Our models explain very little variance (Nagelkerke R² = 0.078 for overall trip frequency; 0.048 for shopping frequency). This low explanatory power is itself a diagnostic signal—and a core motivation for our hypothesis-generating approach—indicating that unmeasured psychological (attitudes, perceived behavioral control), social (networks, norms), and technological (e-commerce) factors likely dominate travel decisions—directing future research priorities. Methodologically, the 2012 data provide an uncommon early-stage baseline for policy-induced resettlement, enabling future longitudinal comparisons and mechanism identification (e.g., before/after metro or mall openings). For contemporary policy, the findings suggest a testable hypothesis that anchor-type facilities (e.g., mid-sized community hubs with mixed use, rather than merely small supermarkets) may activate suppressed demand, requiring re-evaluation with recent data and causal designs. Readers should not extrapolate numeric trip rates to today’s Shanghai. The primary contributions are theoretical (type-based threshold effects, the anchor-vs.-routine facility distinction, the role of basic spatial awareness) and methodological (historical baseline). The 2012 data predate widespread e-commerce; digital substitution may substantially attenuate these relationships in contemporary contexts.

This study aims to identify recent advancements in agent-based modeling (ABM) within the field of travel demand forecasting. To achieve this, peer-reviewed articles published between 2020 and 2024 were sourced from leading academic databases, including Web of Science, Scopus, TRIP (Transportation Research Integrated Database), and ScienceDirect. A total of 1,360 papers were initially retrieved. After a thorough review, 16 papers were selected for detailed analysis based on their strong alignment with the study's objectives.The selected studies were analyzed to uncover key advancements in the application of ABM to travel demand forecasting. Four main areas of progress were identified: (1) spatial-temporal demand modeling, (2) incorporation of multiple influencing factors, (3) utilization of diverse data sources, and (4) integration of multiple frameworks.To the best of our knowledge, few review studies have specifically addressed the use of ABM for travel demand forecasting. The insights from this research provide a foundation for further improvements to ABM, enabling more accurate and robust travel demand forecasting in future studies.

Introduction: The oldest olds (aged 85 and over) are the fastest-growing age segment. However, our understanding of their mobility is limited. To address this gap, we invited 19 U.S. and 30 Chinese “oldest old” to take part in focus groups and complete a mobility questionnaire. We focus on travel mode choice, which includes changes in travel modes, frequency of usage, and perceptions of comfort.Methods: Older adults’ familiarity and acceptance of new mobility technologies (e.g., ridesharing, carsharing, and autonomous vehicles) were measured by questionnaire and focus group. Word clouds were also used to illustrate people’s reasons for choosing their primary mode of transportation.Results and discussion: The results show that both panels of older adults similarly feel some extent of travel limitations. But the responses among the two groups differ: 18 American participants chose “drive myself” as their primary option a decade ago, while 11 chose it now; no Chinese participants selected it either a decade ago or now. Both currently and 10 years ago, there was a significant difference in mode choice between participants in China and the United States. However, this gap has narrowed over the past decade. Participants in China have significantly changed their transportation preferences compared to 10 years ago, while participants in the US have remained nearly unchanged. American respondents consider “ease” as an important factor, while Chinese respondents pay more attention to “safety” and “no other option to get around” when making travel mode choices. Compared to Chinese participants, American participants were more comfortable with driving an autonomous vehicle. These differences may result from the various developmental stages and transportation policies of the two countries. This study supports the development of new mobility technologies for the oldest old to improve their quality of life.

In Chinese megacity centers, substandard housing is being removed. The inhabitants are relocated to peripheral large-scale residential areas, and we call them “relocatees”. After the relocation, their housing quality is improved but travel quality may worsen. Little attention has been paid to the change in travel equity over time for shopping-related trips of relocatees and non-relocatees in these areas in rapidly developing countries, especially for the change before and after the major built environment development, such as a shopping plaza’s opening. To fill this research gap, we conducted two surveys before and after Wanda Shopping Plaza’s opening. We used statistical analysis, estimated discrete choice models focusing on mode choice, and calculated consumer surplus as a measurement of travel equity. Results show a relocatee has a lower travel quality for shopping-related trips. Travel equity between relocatees and non-relocatees for shopping-related trips becomes worse after Wanda’s opening. Differences in consumer surplus between relocatees and non-relocatees provide a reference for the travel subsidy policy.

Chinese big cities are reforming the old downtown and substandard housings are being demolished. The government relocates residents to large-scale residential areas on the city periphery, where residents often find transport service unsatisfactory but still use transit for their most frequent trips. Very few studies have focused on suppressed travel, especially suppressed travel mode choice in this kind of area. To understand the travel mode choice suppression and provide policy suggestions for the area, this study conducts a survey in Jinhexincheng, describes the travel mode choice condition, estimates an integrated mode choice and latent variable model, analyzes transit satisfaction scores, and provides transit improvement suggestions. Results show 41 percent of the resident samples use transit for their most frequent trips, but only 18.55 percent find transit service quality satisfactory. 71.77 percent of the transit rider samples acknowledge that they “have no other choices and must travel by transit.” The integrated choice and latent variable model indicates that the “must travel by transit” feeling (significant latent variable) has a negative effect on transit mode choice. The retired tends to have less of this feeling. Transit satisfaction scores show that “car crowdedness during peak hours” and “most-frequent trip’s travel time” is the most unsatisfactory aspects of the transit service. They should be improved first to prevent “must travel by transit” riders from giving up transit. This supports transit decision-making in developing countries’ similar areas.

In order to reform old downtown and improve living conditions, poor houses in Shanghai are demolished and the government relocates the residents. Usually they are relocated in large scale residential areas in the periphery of the city. Jinhexincheng is one of them. Previous studies usually focused on travel behaviors in the developed countries. Few studies looked into mode choices and preferences in large scale residential areas in the suburbs in the developing countries. In order to fill the gap, this study analyzes three groups’ (overall samples, city-center-old-house-demolition movers, and non-movers) mode choices, preferences, and predicts mode shares in different policy scenarios. It can provide a deeper understanding of this issue and support transportation planning in large Chinese cities. 320 valid samples are collected. Three groups are different in age, Hukou, apartment ownership, and years living in Shanghai. Mode-choice-multinomial-logit models are generated separately for three groups. All models show that males tend to travel by car. For overall and mover samples, for longtime residents, the less likely a person will uses a bike, motorcycle/moped, or transit. For non-mover samples it is not significant. The movers have a higher value of time (1.61 RMB/minute) compared to other groups. For travel time and cost, only the overall model has an acceptably significant travel time coefficient, this model is used to predict mode shares in 4 sets of travel-time-related-policy scenarios. Results suggest that “make bikes available to movers/all residents”, “make shopping shuttle bus more efficient” and “improve transit service” are good policies and can be accepted in transportation planning.

Shanghai is in the process of urban redevelopment. In order to reform old downtown and improve living conditions, poor houses are demolished and the government relocates the residents. Usually they are relocated in large-scale residential areas in the periphery of Shanghai. Jinhexincheng is one of them. Data was collected through a random sampling survey. The study is conducted with two main social groups: workers (representing commuters) and the elderly (representing non-commuters). Results show that a large proportion of workers commute across districts while the elderly often travel inside the district. Our logistic regression model is suggesting the most important factor that causes cross-district commuting is education. Mode choice and morning departure time are different between the workers and the aged. Compared to the world average commuting time (40 minutes), Jinhexincheng's commuting time is quite long. The results suggest that transportation planning must consider travel needs of different social groups.