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Most bicycle accidents are inextricably bound up with risky riding behaviors, which crossing the street illegally at unprotected mid-block locations is nothing to sneeze at. Compared with cyclists crossing the street at the crosswalk or intersections, there is a huge risk of accidents when they ignore or disobey road rules and across recklessly. Yet, the misbehavior of cyclists is an under-explored area in cyclist research due to the limited availability of detailed cycling data. This study creatively develops a GPS-based detection framework to capture risky street-crossing actions for the cyclists from large-scale bike sharing trajectory data. A data-driven modeling approach, based on structural topic modeling (STM), is developed to reveal the complexity and regularity of cyclists' habitual risky crossing behavior. Since objective built environment is one of the key factors associated with cycling, another goal of this paper is to apply a gradient boosting decision tree (GBDT) model to disentangle how the features of built environment may influence the frequency of risky crossing events. The case study results show that risky street-crossing behavior is prevalent in bicycle traffic – for example, 16.94% of cycling trips are involved in illegal crossing action. Most cyclists engage in illegal crossing behavior at the approximate central part of the streets and during the day, which reveals the presence of heterogeneity over space and time. Strong correlations between commuting activities and risky street-crossing behaviors are identified from topic modeling. Meanwhile, the latent illegal crossing patterns unraveled here highlight that typical reasons for committing the risky riding action include the lure of the travel destination across the road and the inconvenience of riding round in distant legal crossing facilities. GBDT findings provide new insights on the existence of the association between built environment and cyclists' illegal crossing action. The places related employment and catering play a dominant role in contributing risky street-crossing behavior, and the influences of road length, road level, bus stop and metro station are not neglectable. Most built environment attributes show nonlinear correlations with crossing frequency. It is anticipated that this study would successfully shed a first light on the pattern of cyclists' risky street-crossing behavior at the metropolitan scale, and compliment engineering practices to improve crossing behaviors and bicycle safety. ISSN:0966-6923 ISSN:1873-1236

Intermodal transfer patterns could provide a better understanding of urban mobility in an integrated transit system. However, the existing literature is still limited related to in-depth data-driven investigations of transfer patterns, particularly between metro and bus. In this study, we propose a generic framework to unravel latent transfer patterns at both aggregated and disaggregated levels. K-means clustering method is first used to classify metro stations based on surrounding built environment. We then introduce the cube technique which allows hierarchical aggregations for each dimension and cross-tabulations of massive ridership data. Last, we redesign the generative mechanism of the structural topic model that is capable of capturing the variability and interdependence in transfer patterns with passenger-level attributes. An empirical study is conducted with large-scale metro and bus smart card data, as well as built environment data in Nanjing, China. The results indicate intermodal transfers exhibit significant heterogeneous patterns in different transfer types over space and time. Although transfers per station in central business district (CBD) areas rank the highest, urban sprawl helps increase the utilization rate of transfer services in inner suburbs with most types of transfer behaviors. Strong correlations are identified among commuting transfer patterns, while correlations remain weak for pattern pairs between commuting and non-commuting activities, reflecting high consistency among commuting activities. Adults play a dominant role in commuting patterns in CBD areas and inner suburbs. Yet, students and the elderly are found to influence the pattern prevalence more effectively in outer suburbs and periphery areas.

People are more likely to choose to take a bus for travel theoretically if both the origin and destination are located near bus stops. However, many of these trips are now replaced by online car-hailing service in China. This study makes the first attempt to look into this special hailing behavior by conducting a contrast analysis between hailing trip and its corresponding bus journey with the same origin and destination. We proposed an identification approach to classify the special hailing trips into four types of hailing behaviors with various hailing willingness. Structural equation modeling is then used to explore the impacts of land use characteristics, features of transport facilities and hailing ridership characteristics on the performance of special hailing behavior. Based on the results, 10.86 percent of special hailing trips have origins and destinations that are located within 100 m distance of the bus stop. Imbalance travel demand for special hailing at different periods of the day indicates the time-varying characteristic, which reaches its highest level in the afternoon. Especially through the major mobility trends of these trips, the spatial patterns of special hailing behavior can be divided into two regimes (i.e. urban and suburban areas) corresponding to the agglomeration area of varied social activities and residential areas. The generalized travel costs of bus and online car-hailing reflect the hailing willingness of travelers to some extent. People face the tradeoff between the two travel modes and tend to choose instead to use online car-hailing in different situations, and the SEM results support this. Notably, the accessibility of bus stops and users' bus travel experience show significant negative effects on special hailing behavior, and the need to transfer during a bus journey can particularly influence a traveler's initiative to take a bus. The findings provide insights into the special online car-hailing usage patterns at bus stop level and its role in urban transport, which can support accurate planning and upgrading of sustainable public transit to increase competitiveness at the big picture.

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

Although metro systems are established in many Asian cities including Chengdu, they have yet to cover every corner of a city. Understanding the transfer behavior of passengers can provide insight into achieving efficient and sustainable urban transport systems. Combining shared mobility programs with metro to improve the weaknesses of traditional feeder modes is viewed as the most promising line of business in sustainable transportation for the near future. Therefore, this study aims to comprehend the factors affecting the usage regularity of shared mobility by deepening the knowledge on endogenous and exogenous effects, and integrating two modes, namely bike-sharing and ridesourcing. Two systems are cross-compared, first in respect of their travel characteristics. Then, a binary logistic model is employed to capture the influences of trip characteristics and travel environment characteristics on their usage frequency. Researchers found that trip distance is significantly associated with users’ mode options, indicating that bike-sharing and ridesourcing mainly serve short-distance and long-distance transfer users, respectively, although some users may be confused which feeder mode to choose for the journeys of 2 km to 4 km. There were also meteorological and temporal influences, with the competition and complementation of multiple shared mobility feeder modes being likely to change under extreme weather conditions, during peak hours, or on weekends. Besides, metro-shared mobility users value the accessibility of two kinds of transport service, which is affected by the metro station and its surrounding built environment. This study and the proposed policy implications are helpful for embracing a sustainable mobility design from general optimum.

Prearranged and on-demand ride services that match passengers and drivers using smartphone applications over a network have developed rapidly across the world. However, the maximum efficiency of the entire hailing demand market and the driver deserve further attention to identify potential improvements. This paper makes the first attempt to conduct a quantitative analysis of the intangible costs generated from ridesourcing trips using an observed ridesourcing dataset provided by Didi Chuxing, including the waiting time costs attributable to waiting for the passenger and the future opportunity costs of different Trip Order Markets. Then, a quantitative method is developed to calculate the specific value of the above intangible costs of each ridesourcing trip. The results show that the average cost of a trip drops by 9.64% when intangible costs are considered, and a large difference exists among the values under various spatial-temporal conditions. In addition, the relative relationship between two types of intangible costs shows a large discrepancy in some sensitive areas. The findings of this study can help transit agencies better understand ridesourcing service and develop strategies for setting reasonable prices and allocating capacity.

Extensive research has shown that unilateral optimization of transit systems is not effective enough to significantly increase its transport efficiency. Considering that urban land-use characteristics, including residential, work, consumption, transit, and so forth, are significantly interrelated with travel demands and travel behaviors, this paper provides a way to optimize transit system by raising awareness of the relation between ridership and built environment. This paper adopted point of interest (POI) data to investigate the effect of physical built environment on online car-hailing ridership in Chengdu, China. The study area was tessellated with several Voronoi cells; these cells were further clustered into three ridership patterns based on the time-varying characteristic of ridership. Given that some differences existed in the three ridership patterns, a separate spatial ridership model was developed to understand the factors that influence ridership patterns using geographic weighted regression (GWR) analysis. The data and results verified that the built environment had various influences on online car-hailing alighting ridership in spatial and temporal dimensions, of which the significant POI factors for determining the ridership pattern during different periods were detected. Remarkably, this study took the ridership dataset from the online car-hailing transit system, mainly because the pick-up (PU) and drop-off (DO) locations generated by this service are closest to the origin and destination of the trip, except that it is more popular recently. Therefore, the analysis of the impact of built environment on travel based on the online car-hailing dataset can be captured in greater detail, with a higher degree of accuracy.

The objective of this study is to investigate impacts of illegal crossings on vehicle operations at unmarked midblock locations. In this study, we first used a speed testing method to examine the s...

Public transportation is regarded as a mitigation measure for addressing climate change and air quality deterioration. However, it is still necessary to estimate the emissions of transit buses, in ...

In order to reduce passenger delays and prevent severe overcrowding in the subway system, it is necessary to accurately predict the short-term passenger flow during special events. However, few studies have been conducted to predict the subway passenger flow under these conditions. Traditional methods, such as the autoregressive integrated moving average (ARIMA) model, were commonly used to analyze regular traffic demands. These methods usually neglected the volatility (heteroscedasticity) in passenger flow influenced by unexpected external factors. This paper, therefore, proposed a generic framework to analyze short-term passenger flow, considering the dynamic volatility and nonlinearity of passenger flow during special events. Four different generalized autoregressive conditional heteroscedasticity models, along with the ARIMA model, were used to model the mean and volatility of passenger flow based on the transit smart card data from two stations near the Olympic Sports Center, Nanjing, China. Multiple statistical methods were applied to evaluate the performance of the hybrid models. The results indicate that the volatility of passenger flow had significant nonlinear and asymmetric features during special events. The proposed framework could effectively capture the mean and volatility of passenger flow, and outperform the traditional methods in terms of accuracy and reliability. Overall, this paper can help transit agencies to better understand the deterministic and stochastic changes of the passenger flow, and implement precautionary countermeasures for large crowds in a subway station before and after special events.

This study proposed a hybrid optimization model for urban bus transit route network design problem (TRNDP). Although several mathematical methods had been developed to make the problem tractable, the methods relied on excessive assumptions, which resulted in over-simplification or idealization of the problems. In light of these considerations, a multi-level and multi-mode network design method was introduced in this study. A multi-level network consisted of three levels: skeleton network, arterial network, and feeder network. The different levels of network were associated with different modes of public transportation (such as subway, light rail transit, trolleybus, BRT, normal bus, and community branch) based on the features of bus routes, city sizes, etc. In addition, according to the respective features of urban transit route network structures, we developed different optimization models for different levels. Finally, the proposed methodology was applied to case studies of the city of Zhaoyuan in China and the transit network of Mandl benchmark. The results showed that the total travel time for the proposed method was significantly lower than that of the competing method, with a 21.51% reduction. In addition, the proposed method provided 85.23% direct travelers, 14.65% travelers with one transfer, 0.12% travelers with two transfers, and no unsatisfied demand, which were better than the results from the compared method.

Urban transit buses equipped with large-displacement engines operate on circular routes several times throughout the day, emitting large amounts of environmentally hazardous exhaust. Hence, understanding the intricate associations between bus emissions and multiple contributors is beneficial for creating sustainable transportation systems, while previous studies focusing on statistical methods fail to unravel them. This paper innovatively leverages the bagged decision tree approach to delineate such complex relationships based on the data collected from CNG-fueled and diesel-powered buses. Relative importance indicates that velocity appears to be the primary factor and is therefore selected as the research objective. Results suggest that the effects of different contributors on bus emissions present nonlinear patterns. More specifically, the influence of speed on CO, CO2, and NOx exhaust generally reveals an increasing-stabilizing tendency while that of HC represents a decreasing-stabilizing mode. Besides, the phenomenon of synergies between determinants is also prevalent, for instance, buses within high-speed and large-slope conditions tend to produce more emissions. These findings can provide nuanced guidance for policy-making and bus route planning issues in consideration of environmental protection and pollution mitigation.

Operators of personal transport units (PTUs) face the challenge of intelligently balancing the locational demand and supply of PTUs in order to mitigate surpluses or deficits at PTU pickup ...