The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s² to 4.5 m/s², the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.

Lane changing poses a great threat to traffic safety in the work zone areas. This study aims to conduct an in-depth investigation of drivers' lane-changing behavior and to propose and verify the efficacy of an intelligent in-vehicle audio prompt through a driving simulator experiment. Forty-two participants, comprising 22 males (12 professional drivers and 10 normal drivers) and 20 females (10 professional drivers and 10 normal drivers), took part in the study. Three traffic density levels (20pcu/km, 40pcu/km, and 60pcu/km) and five locations (baseline, 250m, 500m, 750m, and 1000m) to release the audio prompt information were set to collect eye movement and driving behavior data. A multistage lane-changing process including the perception stage, preparation stage, and action stage was developed, and three types of variables, namely experiment variable, behavioral variable, and risk assessment variable, were defined. The results indicated that the audio prompt set at 500m, 750m, and 1000m could noticeably improve lane-changing behavior (i.e., triggering lane changes more in advance and providing drivers with more preparation time). However, the audio prompt set at 250m upstream of the work zone showed limited efficacy, which is not recommended in practical application. Male drivers and professional drivers performed better during lane changing, and they had a more active observation of the surrounding environment and tended to adopt safer vehicle operations than their counterparts. A high traffic density could increase the collision risks even if drivers had made sufficient preparations, while in low traffic density conditions, drivers who failed to change lanes tended to apply hard braking when they approached the work zone.

Subway station-level peak hour ridership (SPR) is a crucial input parameter for multiple applications, including the planning, design, construction, and operation of stations. However, traditional SPR estimation techniques may produce biased results. A unified peak hour factor (PHF) extracted from the line level is generally set for all attributed stations, which ignores the possible peak deviation that arises between the station and line and the wide variation of PHFs in practice. This study presents a comprehensive and refined estimation framework for SPR that accommodates the peak deviation context by introducing the peak deviation coefficient (PDC). Moreover, the estimation of the PDC and PHF variability is improved by constructing spatial regression based relationship models. The empirical results show that the proposed approach exhibits wider applicability and a higher prediction precision across all types of peak periods considered as compared to conventional methods (i.e., MAPE decreases of 0.115–0.351). The findings demonstrate the importance of the consideration of the peak deviation scenario and the spatial dependency in SPR estimation to achieve better decision making. Moreover, the underlying influencing mechanism of the PHF and PDC at distinct peak periods is further revealed using the spatial model. This provides critical theoretical references and policy implications to prudently deploy land-use resources to balance the travel demand between peak and off-peak periods and thus enhance the line operation efficiency.