Abstract ：

Traffic conflict commonly occurs in intersections with offset lane lines because of mismatched choices of entrance and exit lanes. This conflict drives down overall traffic efficiency and can cause crashes. At such intersections, the driver's lane choice behavior seriously affects the traffic system. The theory of planned behavior (TPB) was used in this study to excavate the impact mechanism of drive-by-lane behavior at offset intersections. A scale incorporating TPB constructs and additional variables (risk perception [RP], traffic environment [TE], optimization measures [OM], and driving style [DS]) was developed to collect empirical data with 557 valid samples in China. A structural equation model of drivers' drive-by-lane behavior was established to explore the causal relationship between behavior and influencing factors, as well as the interrelationships between TPB variables. TE, behavior intention (BI), subjective norms (SN), and RP show the most significant effects on drive-by-lane behavior. DS, perceived behavior control, attitude (ATT), and OM have little impact on drive-by-lane behavior. BI, TE, RP, and DS have significant, direct effects on drive-by-lane behavior. The TE, OM, and DS variables significantly affect drive-by-lane behavior through mediators. RP, BI, and ATT mediate the influence of other variables as well as affecting drive-by-lane behavior. We put forward suggestions to improve driving behavior at offset intersections including changing external factors, education, and training. The direct and indirect relationships between the influencing factors and drive-by-lane behavior are discussed in this paper to provide technical and reference support for future research on managing irregular urban traffic intersections.

Keyword ：

bicycles cognitive workload data and data science driver perception human factors of infrastructure design and operations modeling pedestrians analysis statistical methods human factors

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Abstract ：

Objective More attention should be given to bicycle-motor vehicle (BMV) crashes, as cyclists are at a higher risk of suffering injuries than motor vehicle users in a crash. This study aims to explore the factors influencing the injury severity of bicycle-motor vehicle (BMV) crashes in Beijing (China) and discusses the commonalities and differences between the urban and suburban areas. Methods Information regarding 1,136 crashes between bicycles and motor vehicles were collected using police reported data from 2014 to 2015. A two-stage approach integrating random parameters logit (RP-logit) model and two-step clustering (TSC) algorithm was proposed to investigate the significant influence factors and their combination characteristics. Specifically, the RP-logit model was first used to identify the significant influence factors of urban and suburban areas, and then the TSC algorithm was applied to reveal the combination characteristics of significant influence factors for the fatal crashes. Results Five factors were found to be statistically significant and had random effects on the injury severity in urban areas, i.e., type of motor vehicle, motor vehicle license ownership, type of bicycle, signal control mode and lighting condition; and seven factors were found to be statistically significant on the injury severity in suburban areas, i.e., type of motor vehicle, motor vehicle license ownership, physical isolation facility, signal control mode, weather, visibility and lighting condition. Based on TSC, the combination of significant factors showed different characteristics for fatal crashes in urban and suburban areas, in which two types of the scene including five factors should be concerned in urban areas while one type of scene containing four factors in suburban areas. Conclusions The results suggest that different influence factors and individual heterogeneity exist in the RP-logit model for injury severity analysis of BMV crashes in urban and suburban areas. It shows that in urban areas, heavy truck, light truck and bus significantly increase the likelihood of fatal injury than that of suburban areas. These findings can provide valuable reference information for BMV crashes response, such as heavy truck restriction, to facilitate regional safety measures for urban and suburban areas.

Keyword ：

random parameter logit model urban and suburban areas bicycle-motor vehicle crashes two-step clustering algorithm Injury severity

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Abstract ：

Mopeds (electric bicycles and light motorcycles) are commonly used as a personal transportation mode in China. However, the understanding of characteristics of left-turning mopeds at signal-controlled intersections has been relatively limited. To bridge this gap, firstly, this paper proposed a video conversion method of moped movement data acquisition. Then, a method of data accuracy verification was introduced by comparing the results between the field experiment and the video conversion method. Secondly, the ideal traffic space for left-turn mopeds from different entrances was defined to analyse the characteristics of the left-turning mopeds at intersections. Further, three indicators, namely, transverse distance, the proportion of left-turning mopeds with crossing behaviour, and the average number of avoidance behaviour, were proposed and used to analyse the asymmetrical characteristics behaviour, crossing behaviour, and avoidance behaviour. Finally, based on empirical data collected from five signal-controlled intersections, the proposed methods and behaviours were analysed. This paper provides both a valid method of obtaining the position data of mopeds and a reliable basis for improving the safety of left-turning moped riders at urban signal-controlled intersections.

Keyword ：

left-turning moped traffic flow signal-controlled intersection traffic engineering riding behaviour

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Abstract ：

The bicycle is a healthy and sustainable transport mode due to its emission-free characteristics. To increase bicycle use, it is fundamental to provide bicycle-friendly environments. To better monitor bicycle environments, this study proposed the concept of bicycling environment quality (BEQ), which was defined by perceived satisfaction and conflict level. Data collection was conducted at 19 road segments in five sites located in Beijing, China. Then, speed-related and acceleration-related bicycling behavior indicators (BBIs) were extracted from data collected using sensors on mobile phones, while bicycling environment indicators (BEIs), such as bicycle flow, were extracted from recorded data. Taking the BBIs and BEIs as input attributes, a two-level BEQ classification assessment model based on a random forest (RF) algorithm was constructed. The proposed RF-based classification assessment model was able to produce approximately 77.35% overall correct classification. The results demonstrate the feasibility of using GPS data in evaluating BEQ. In addition, a novel dockless bicycle-sharing system (DBS)-based framework for bicycle traffic monitoring is discussed, which is of great significance in the sustainable development of bicycles. This study provides a theoretical method for objective BEQ assessment. It can further be used by planners and road administrators to monitor and improve BEQ and by individual cyclists for optimal route choice.

Keyword ：

random forest bicycling environment quality monitoring classification mobile sensor

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Abstract ：

Dockless bike sharing plays an important role in complementing urban transportation systems and promoting the sustainable development of cities worldwide. To improve system operational efficiency, it is critical to study the spatiotemporal patterns of dockless bike sharing demand as well as factors influencing these patterns. Based on bicycle trip data from Mobike, Point of Interest (POI) data and smart card data in Beijing, we built a spatially embedded network and implemented the Infomap algorithm, a community detection method to uncover the usage patterns. Then, the Gradient Boosting Decision Tree (GBDT) model was adopted to investigate the effect of the built environment and public transit services by controlling the temporal variables. The spatiotemporal distribution shows imbalanced characteristics. About half of the total trips occur in the morning/evening rush hours and at noon. The community detection results further reveal a polycentric pattern of trip demand distribution and 120 sub-regions with a significant difference in connection strength and scale. The result of the GBDT model indicates that factors including subway ridership, bus ridership, hour, residence density, office density have considerable impacts on trip demand, contributing about 62.6% of the total influence. Factors also represent complex nonlinear relationships with dockless bike sharing usage. The effect ranges of each factor were identified, it indicates rebalancing schemes could be changed according to spatial location. These findings may help planners and policymakers to determine the reasonable scale of bike deployment and improve the efficiency of redistribution in local regions while reducing rebalance costs.

Keyword ：

Dockless bike sharing system Bicycles Spatiotemporal phenomena Urban areas community detection Public transportation gradient boosting decision tree built environment spatiotemporal patterns Roads Meteorology

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Abstract ：

As bicycling regains popularity around the world, the Beijing Public Bikesharing System, launched in 2012, enables users to access shared bicycles for short trips. After five years of operation, while the system is widely used, it faces the problems of bike unavailability and dock shortage at various stations due to the tidal characteristics of bicycle travel. It is necessary to investigate the influence of different weather conditions and nearby built station environments on bikesharing trips. Using historical trip data from 2016 concerning 543 stations in Beijing, log-linear regression models are developed to estimate the impact of daily weather and time events on bikesharing trips. Moreover, the effects of built environment variables, such as land use and transport infrastructure, are investigated both on workday and non-workday usage at the station level. The results indicate that temperature is not linearly associated with daily usage. Daily usage decreases according to rainfall, snowfall, wind speed and weekends/holidays. Light and heavy pollution have no significant influence on bikesharing demand; however, severe pollution has a negative influence on usage. The effect of transport infrastructure (subway stations, bus stops and bikeway length) is crucial in increasing bikesharing demand. The number of residential and shopping locations is generally associated with usage. Proximity to colleges does not show an obvious usage increase, which is different from the results obtained in other cities. Parks encourage more bikesharing usage on weekends/holidays than on workdays. The findings may help planners or managers to design and modify public bikesharing stations effectively, increasing usage while reducing rebalance costs.

Keyword ：

Temporal factors Public bikesharing Daily weather Land use Log-linear regression Transport infrastructure

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Abstract ：

As a clean, sustainable transport tool, bicycles have significant advantages in short-distance travel. Despite many efforts assumed in Beijing to improve the cycling environment, the popularity of cycling remains relatively low. However, the advent of the free-floating bike-sharing (FFBS) system has engendered an unexpected cycling enthusiasm in Beijing. Therefore, it is of great importance to delve into why travelers prefer FFBS as a transportation form from a psychological perspective. In this paper, 352 valid questionnaires were collected from an online survey, and an extended theory of planned behavior (TPB) was adopted to examine the psychological determinants of intention and actual behavior to use FFBS. The results showed that men and car-owners prefer vehicles and show a lower willingness to use FFBS. In contrast, residents under the age of 60, residents with FFBS riding experience, and residents skilled in cycling are inclined to use FFBS; the economic convenience of FFBS is the most important attractant for FFBS, while bad weather is the biggest hindrance factor for residents to use FFBS; however, imperfection in infrastructure has no significant impact on reducing residents' willingness to use FFBS. These results have important implications for planners to better understand the FFBS use behavior, and several suggestions are proposed to support the policymaking about FFBS.

Keyword ：

social-psychological variables free-floating bike-sharing system use frequency influence factor intention

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Abstract ：

The emergence of the Free-Floating Bike Sharing System (FFBSSs) has brought convenience to the public and also posed new challenges to urban construction and management. Inspired by the ability of Markov chains to handle large volumes of data in Google’s PageRank algorithm, we propose a Markov-chain based approach to model the FFBSSs for capturing its macroscopic aggregated properties. The geohash based algorithm is proposed to divide a geography map into cells due to the non-stock feature of the FFBSSs. After this, the transition matrix of the Markov chain is built based on historical bike trip data. Spectral clustering properties and the characteristic that Kemeny constants can identify the critical regions are discussed. Then we use about 3.2 million bike trips real data of BJUT Beijing, China from Mobike to demonstrate its application in identifying clusters and critical stations. In our empirical study, three clusters are identified in the vicinity of the BJUT, one of which is further analyzed and then 10 critical cells corresponding to the major sites in the cluster are identified, which is in line with reality. © 2019, Springer Nature Switzerland AG.

Keyword ：

Bicycles Big data Clustering algorithms Markov chains

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As a vehicle that stands for the development of new technology, and an effective solution for energy shortage and environmental problems, electric bicycle has not only entered thousands of households but also become the first choice for short-distance travel in many cities in China. Due to its flexibility and convenience, electric bicycle is often involved in unsafe traffic behaviors such as running red light and crossing motor vehicle, therefore disrupt the traffic order at the intersection, which has not only lowered the service level of the intersection and road section but also increased the traffic accident rate. From the perspective of traffic safety, this research first studies the characteristics of the Electric bicycle at the intersection, puts forward the method of calculating the electric bicycle flow at the intersection, and analyzes the delay time of the Electric bicycle after conducting the actual survey, then describes the releasing process of the electric bicycle according to the practical observation, and calculates its dilatation during the releasing process. In the end, the research studies the electric bicycle’s queuing process, and the relation between its queuing density and queuing width at the intersection combing with real-life investigation. The research result provides a great reference value in solving the traffic problems with electric bicycle in the urban transport system. © Springer Nature Singapore Pte Ltd. 2019.

Keyword ：

Accident prevention Accidents Environmental technology Intelligent systems Sporting goods Urban transportation Intelligent vehicle highway systems Bicycles Electric bikes Electric vehicles Queueing theory

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As a sustainable mode of transportation, public bicycles significantly improve daily mobility. The location of stations is a key element for the success of a public bicycle system, as a long walking distance will reduce people's willingness to use this mode of transportation. Building forms in China are different from the open type seen abroad. Many residential, office and school areas are enclosed by walls, and pedestrian flow is concentrated at the entrances/exits of these areas. Therefore, the station must be located close to the building entrance/exit. Previous studies on station location located the stations only per zone, without providing the exact locations of the stations in the zones. This paper considers the optimal distance between the building entrance/exit and the station to determine the exact station locations. The results can serve as a reference for the planning and optimization of public bicycle stations. A questionnaire survey was conducted in Beijing to determine users' walking distances to the stations. The results indicated that the walking distance decay laws of stations were different for different land uses. Moreover, a binary logistic model was developed to verify that users with different travel purposes have different walking distances. Based on the above results, we explored the optimal distances and tolerable distances between the building entrance/exit and the station for different land uses. These distances can be used to determine exact station locations to meet users' physiological and psychological needs.

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