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Bus bunching is of particular concern and undesirable for both operators and passengers in high-frequency transit. In contrast to existing control methods, this paper proposes a novel control policy, namely, providing real-time bus crowding information (BCI) for passengers. It is believed that passengers would spontaneously distribute more evenly among buses and help to prevent bus bunching with the provision of BCI accompanying arrival time information due to the following mechanism. A proportion of passengers would be likely to wait a few more minutes for the next bus when the current bus is crowded and the next bus is more comfortable, and the boarding times of these passengers would make the next bus dwell longer and increase its headway from the previous bus. We formulate bus motion models incorporating passenger boarding choice under BCI to realize the policy in simulation experiments. The results demonstrate that the policy can reduce operation instability by approximately 20% in terms of bus headway and single-trip time. In addition, this policy can significantly reduce the in-vehicle crowdedness experienced by passengers by up to 25% at the cost of small increases in the mean journey time in some cases. The simulation experiments on a holding-controlled route also indicate that the proposed policy is able to coordinate with holding well. A sensitivity analysis further confirms that the policy’s performance is robust even if the passengers have low inclinations to choose the next bus. The policy of providing BCI in this paper is especially effective for bus routes with high passenger demand and may have great application potential in practice.

Integration between bike sharing and rail transit provides users with a more flexible travel pattern in an effort to address the “first/last mile” problem. This study aims to examine the determinan...

In high‐frequency transit, providing real‐time crowding information (RTCI) is a potential way to promote passenger satisfaction and reduce negative crowding externalities, by assisting passengers in choosing less crowded vehicles. To make RTCI convincing and reliable, it is necessary to provide predictive RTCI, in which bus passenger load (BPL) prediction is the primary problem. This paper proposes a novel two‐stage BPL prediction method using automatic passenger counting (APC) data. The first stage is to predict short‐term passenger flows at stops based on an adaptive Kalman filter approach. Using the outputs from the first stage as well as other variables directly from APC data, the second stage is to predict BPL based on a support vector regression algorithm. Several methods from the existing literature are used as benchmarks to test the relative performance of the proposed method. An empirical study on bus line 1 in Suzhou, China shows that the proposed method outperforms all the benchmarks, and shows significant superiority over other methods for stops with sharp increases in BPL and for multi‐step ahead prediction. This study contributes to the limited literature on BPL prediction and lays the foundation for providing accurate and reliable predictive RTCI in the future.

Dockless bike-sharing (DBS) is a novel and prevalent bike-sharing system without stations or docks. DBS has the advantages of convenience and real-time positioning, whereas it brings about some problems such as bike over-supply, disordered parking, and inefficient rebalancing. Forecasting usage and bike distribution are critical in the rebalancing operation for maintaining DBS inventory. By dividing the virtual stations through K-means clustering and processing the four-week Mobike journey data of Nanjing, China, the data of usage and bike count in the 4000 virtual stations are identified. Random forest (RF) is developed to predict the real-time passenger departure, passenger arrival and bike count in the virtual stations. The operation analyses indicate that there is a positive correlation between bike count and usage. RF provides accurate predictions of usage and bike distribution, and almost outperforms five benchmark methods. Forecasting bike distribution is more challenging than forecasting usage because of the volatility of many factors. The results also suggest that bike distribution forecasting based on the usage gap prediction is better than that based on the departure and arrival prediction. This study can help DBS companies in dynamically rebalancing bikes from over-supply regions to over-demand regions in a better way.

The application of travel demand models to transportation planning has triggered great interests in issues that potentially improve the accuracy of model forecasts. These forecasts, however, are su...

China has been witnessing prominent demographic ageing because of its sustained low fertility (one-child policy) and mortality rates. In 2017, nearly one in four elderly adults in the world live in China. The rapid increase of the elderly population is supposed to dramatically influence the urban and transportation system. Active travel plays an important role for the ageing Chinese population to sustain their mobility and wellbeing. To provide suitable policy implications for age-friendly travel environments in China, this study investigates how the built environment affects active travel behavior. Particularly, we explore the influences of built environment on daily active travel frequency and time expenditure while taking into account travel attitudes. A zero-inflated ordered probit model and a Cox proportional hazards model are respectively estimated based on the Nanjing Travel Survey data. Results show that the social and cultural contexts exert pronounced impacts on the travel pattern of Chinese older people. Specifically, it is found that the living pattern of co-residence, and the proximity to market, park/square, and chess/card room are influential in shaping active travel patterns. In addition, the built environment shows larger effects on the active travel behavior of older adults than on that of young people. Attitudes towards active travel are not significant in explaining the senior's travel behavior, indicating limited self-selection effects. The findings will offer insights to establish effective and appropriate land use strategies and public facility distribution for the elderly during the Chinese urban renewal process.

The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands. Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated. The random forest (RF) is a powerful method for constructing an ensemble of random decision trees. It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees. However, the usefulness of RF for travel mode choice behavior remains largely unexplored. This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability. Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated. The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost. In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices. This is fundamental for a better understanding and effective modeling of people’s travel behavior.

Health care accessibility is a vital indicator for evaluating areas where there are medical shortages. However, due to the lack of population data with a satisfactory spatial resolution, efforts to accurately measure health care accessibility among older individuals have been hampered to some extent. To address this issue, we attempt to measure accessibility to health care services for older bus passengers in Nanjing, China, using a finer spatial resolution. More specifically, based on one month's worth of bus smart card data, a framework for identifying the home stations (i.e., a passenger's preferred station near their residence) of older passengers is developed to measure the aggregate demand at the bus stop scale. On this basis, a measurement that integrates the Gaussian two-step floating catchment area (2SFCA) and the adjusted 2SFCA methods (referred to as the adjusted Gaussian 2SFCA method) is proposed to measure accessibility to health care services for older people. The results show that: (1) almost all home stations experience inflated demand, especially those located in the suburbs; (2) despite abundant health care resources, home stations in urban districts are rarely identified as high accessibility stations, due to high demand densities among the older population; and (3) more attention should be paid to two types of home stations – those with a medical institution and those with bed shortages, respectively. The first type is predominantly distributed in the periphery of the city, in the suburbs where the travel time required to access the nearest health care service by bus is longer. The second type is mostly located in the outskirts of urban districts and in the central area of one suburb. These findings could help policy makers to implement more appropriate measures to design and reallocate health care resources.

Dockless bike sharing (DBS), as an emerging bike sharing scheme, effectively promotes active travel and improves the mobility of users. Travel behavior analysis of DBS users is a key basis of service improvement, and the data sources include user surveys and trip records. User surveys are widely applied but have the drawback of perceptual errors. Thus, there is a compelling need for exploring travel behavior using automatically-collected trip records. This study examines DBS users' travel frequency and purpose with five semi-annual user surveys and trip record data in Nanjing, China. User surveys reflect user perception of travel behavior, while trip records can be used to calculate actual travel frequency and infer travel purpose. The results show that perceptual travel frequency based on user surveys has a central tendency, and actual travel frequency based on trip records follows a heavy-tailed distribution. Older people tend to underestimate their travel frequency, but other age groups tend to overestimate their frequency. In addition, a dockless bike sharing's travel purpose inferring (DBS-TPI) model is proposed to infer the travel purpose based on trip record data and points of interest data. The DBS-TPI model is reliable and transferable under different scenarios, which could complement user surveys. This study provides insights into improving data collection for better understanding of travel behaviors among DBS users.

This paper proposes a continuum approximation (CA) modeling framework to optimize the hybrid transit network designed with grids in the central district and hub-and-spoke structure in the periphery. Two CA models are formulated incorporating the local route service and the short-turn strategy respectively. The transit network configuration is optimized through minimizing the objective functions, which consider costs pertinent to passengers and operating agency. The decision variables include service boundary, spacings and headways of the regular service and the complementary services. Numerical experiments show that the performances of CA models with different complementary services are quite distinct under various demand scenarios.

Smart card (SC) data has become one of the major data sources for transit passengers’ behavior analysis, network modeling, and control optimization. Origin–destination (O–D) estimation has been recognized as a requisite step before utilizing the smart card data to investigate transit passengers’ spatiotemporal dynamics or conduct other SC data-based transit modeling. In the recent decade, the extant literature has proposed various trip-chain-based methods for transit O-D estimation using SC data. However, one problem of the conventional trip-chaining estimation approach has been noticed but not paid enough attention to: O-D estimation of single transactions cannot be conducted since the trip-chain method generally requires at least two trip records per day to proceed with. Such a flaw in the classic trip-chain approach might lead to a considerable amount of data loss and inaccurate O-D estimation. This paper improved the existing trip-chain O-D estimation method by introducing a new framework based on the Minimum Entropy Rate (MER) criterion. The proposed MER-based method adopts a similar mechanism of noise reduction in information theory. The basic idea of our approach is to infer the alighting location of single trips using alternative stops that preserve passengers’ travel regularity exhibiting in their mobility sequences. Our enhanced approach can estimate alighting stops for single trips with decent accuracy, thus preventing a potential massive data loss. Moreover, the study also provides an in-depth insight into the relationship between entropy rates estimated using trip sequences and passengers’ travel regularity. The estimation results can further benefit future transit studies with reliable data sources.

Introduction Coronavirus disease 2019 (COVID-19) has triggered a worldwide outbreak of pandemic, and transportation services have played a key role in coronavirus transmission. Although not crowded in a confined space like a bus or a metro car, bike-sharing users are exposed to the bike surface and take the transmission risk. During the COVID-19 pandemic, how to meet user demand and avoid virus spreading has become an important issue for bike-sharing. Methods Based on the trip data of bike-sharing in Nanjing, China, this study analyzes the travel demand and operation management before and after the pandemic outbreak from the perspectives of stations, users, and bikes. Semi-logarithmic difference-in-differences model, visualization methods, and statistic indexes are applied to explore the transportation service and risk prevention of bike-sharing during the pandemic. Results Pandemic control strategies sharply reduced user demand, and commuting trips decreased more significantly. Some stations around health and religious places become more important. Men and older adults may be more dependent on bike-sharing systems. The declined trips reduce user contacts and transmission risk. Central urban areas have more user close contacts and higher transmission risk than suburban areas. Besides, a new concept of user distancing is proposed to decrease transmission risk and the number of idle bikes. Conclusions This paper is the first research focusing on both user demand and transmission risk of bike-sharing during the COVID-19 pandemic. This study evaluates the mobility role of bike-sharing during the COVID-19 pandemic, and also provides insights into curbing the viral transmission within the city.

Low-income commuters have distinct activity-travel characteristics from non-low-income commuters. This study examines low-income commuters’ activity-travel pattern for a better understanding the me...

The built environment affects individuals’ travel behavior in a variety of dimensions, such as trip generation, mode choice, and travel duration. However, it is not well understood how these effects differ across different socioeconomic groups (e.g. the elderly versus younger adults) and how residential self-selection contributes to these differences. Using the 2013 Nanjing (China) Travel Survey data, this study estimates the differential responsiveness to the variation in residential location for different age groups. The two-step clustering method is applied to characterize two types of residential locations and the propensity score matching approach is utilized to address self-selection effects. We find that, after control for self-selection, residential location effects on travel behavior differ significantly between the elderly (60+ years old) and younger respondents (18–59 years old). Changes in the living environment play a more important role in influencing the elderly’s travel frequency and travel duration than those of younger adults. When we compare the observed effects of residential location, self-selection effects are modest for the elderly while they matter to a great extent for younger adults. In addition, due to differences in residential self-selection, there is an underestimation of residential location effects on the elderly’s travel behavior versus an overestimation of those for younger adults. These findings indicate that overlooking the variation of built environment effects between different age groups may lead to ineffective housing and transportation policy implications.

Daily trip chain complexity and type choices of low-income residents are examined based on activity travel diary survey data in Nanjing, China. Statistical tests reveal that non-work trip chain complexity is distinctly distinct between low-income residents and non-low-income residents. Low-income residents are inclined to make simple non-work chains. Two types of econometric models, a stereotype logit model and mixed logit model, are then developed to investigate the possible explanatory variables affecting their trip pattern. The number of stops within a chain and chain types are considered as dependent variables, while independent variables include household and personal characteristics as well as land use variables. Results show that once convenient and flexible conditions are supplied, low-income residents are more likely to make multiple activities in a trip chain. Areas with high population and employment densities are associated with complex work trip chains and more non-work activity involvement.

The station-based bike sharing system (SBBSS) and the free-floating bike sharing system (FFBSS) have been adopted on a large scale in China. However, the overlap between the services provided by these two systems often makes bike sharing inefficient. By comparing the factors that affect the usage of the two systems, this paper aims to propose appropriate strategies to promote their coordinated development. Using data collected in Nanjing, a predictive model is built to determine which system is more suitable at a given location. The influences of infrastructure, demand distribution, and land use attributes at the station level are examined via the support vector machine (SVM) approach. Our results show that the SBBSS tends to be favored in areas where there is a high concentration of travel demand, and close proximity to metro stations and commercial properties, whereas locations with a higher density of major roads and residential properties are associated with more frequent use of the FFBSS. With regard to the methods used, a comparison of several machine learning approaches shows that the SVM has the best predictive performance. Our findings could be used to help policy makers and transportation planners to optimize the deployment and redistribution of docked and dockless bikes.

In this paper, we proposed a new method to extract travel patterns for transit riders from different public transportation systems based on temporal motif, which is an emerging notion in network science literature. We then developed a scalable algorithm to recognize temporal motifs from daily trip sub-sequences extracted from two smart card datasets. Our method shows its benefits in uncovering the potential correlation between varying topologies of trip combinations and specific activity chains. Commuting, different types of transfer, and other travel behaviors have been identified. Besides, varying travel-activity chains like “Home → Work → Post-work activity (for dining or shopping) → Back home” and the corresponding travel motifs have been distinguished by incorporating the land use information in the GIS data. The analysis results contribute to our understanding of transit riders’ travel behavior. We also present application examples of the travel motif to demonstrate the practicality of the proposed approach. Our methodology can be adapted to travel pattern analysis using different data sources and lay the foundation for other travel-pattern related studies.

This paper addresses a static bike repositioning problem by embedding a short-term demand forecasting process, the Random Forest (RF) model, to account for the demand dynamics in the daytime. To tackle the heterogeneous repositioning fleets, a novel repositioning operation strategy constructed on the hub-and-spoke network framework is proposed. The repositioning optimization model is formulated using mixed-integer programming. An artificial bee colony algorithm, integrated with a commercial solver, is applied to address computational complexity. Experimental results show that the RF can achieve a high forecasting accuracy, and the proposed repositioning strategy can efficiently decrease the users’ dissatisfaction.

Destination choice studies have been primarily carried out in developed countries. However in China, a typical developing country, few studies about destination choice exist. In this paper, we propose nonlinear-in-parameters multinomial logit models to investigate the influences of spatial factors on both work and intermediate stop destination choices. We use individual sociodemographics, travel-activity attributes, and land-use characteristics as exogeneous variables. Individual's destination choice behaviors with different sociodemographics and travel modes are examined as well. The models are applied to data collected in the city of Shangyu, China. Compared with previous studies, this research further distinguishes the size variables influencing destination choices for work and intermediate stop in the type and the extent to which each type of size variables exerts influence. Besides, the preferences to destination choices are more clearly illustrated, resulting from the typical occupation characteristics and commuting modes of China.