Non-Symmetric Spatial-Temporal Network for Bus Origin–Destination Demand Prediction

Urban public transport has become a preferred choice for alleviating traffic congestion. The bus passenger OD (origin–destination) demand prediction based on bus operational data is the key technology to realize urban intelligent transportation system. However, most of the existing bus OD demand prediction methods only considered regional passengers. The problem of the OD demand prediction based on historical OD matrices of bus lines is still not easy to implement, exceptionally, which is suitable for most of the urban bus lines. This paper presents a non-symmetric spatial-temporal network (NSTN) based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) network to predict bus OD. NSTN contains the station spatial component (SSC) module and the spatial-temporal component (STC) module. SSC consists of two CNNs to learn the OD features and the DO (destination-origin) features, respectively. To make the prediction shift to the OD features, the non-symmetric input is designed. STC extracts spatial-temporal features based on ConvLSTM. Compared with other methods, NSTN has the best performance measured by symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE), where its SMAPE falls by 4.3 percentage points to 16.4 percentage points and RMSE falls by 23.1 percentage points to 69.9 percentage points. Experimental results on other bus lines show that NSTN has strong generalization ability.

MTCS: A Method of Analyzing the Traffic Capacity of Urban Bus Lane Based on Vissim Simulation

As an effective method of improving the attractiveness of urban public transport and alleviating urban traffic congestion, bus lanes play an important role in the urban public transport system. The research on the capacity of bus lanes is conducive to improve the operation efficiency of urban bus roads and improve the service level of urban public transport. To obtain the maximum capacity of the bus lane, on one hand, the empirical formula can be used for theoretical calculation, and on the other hand, the simulation model can be established for analysis and verification. Based on the idea of simulation, a method using Vissim is proposed, called MTCS (Minimum Traffic Capacity Substitution Method). The method divides the bus lane into different sections by intersections and stops, establishes simulation model of the bus lane to calculate the traffic capacity of each section such as vehicle speed and flow and select the minimum traffic capacity of the sections as the traffic capacity of the bus lane, which is verified by using the road saturation. The simulation process uses the actual travel speed and traffic flow of the bus lane as evaluation indicators, with the aim of maximizing the road traffic flow while the actual speed of vehicles on the road is close to the desired speed, thus achieving the desired road traffic state. To verify and improve the effectiveness of the method, its analysis results are compared with the empirical formula, and various methods of enhancing traffic capacity are quantitatively simulated. The parameters of the simulation model are set by the actual bus lane example, and the experimental results show that by the methods of modifying the stop-station mode and the signal-lamp cycle, 10% and 14% improvements can be achieved, respectively. This has a good reference value for the construction of bus lanes and the adjustment of road facilities.

Occupational Health and Job Satisfaction Assessment of Bus Rapid Transit (BRT) Drivers

Several studies have focused on ergonomics of commercial and urban bus drivers; however, there exists a dearth of research on BRT drivers. This study was conducted to investigate the factors affecting the BRT drivers' mental health and satisfaction. The study was carried out on 171 BRT drivers in Tehran, Iran. The required data were collected through two questionnaires. The Classification and Regression Tree (CART) and Hierarchical clustering (HC) was used to extract factors affecting mental health and satisfaction of BRT drivers. The important factors affecting BRT drivers' mental health were: dispute with passengers, depression, BMI, criminal behaviours of passengers, driver's retirement conditions, driver's family conditions, fatigue and the rostering. In addition, the most important factors affecting driver satisfaction were: bus repairs, driver's seat and the sound inside the cabin. Possible practical application includes: creating a counseling and psychotherapy unit and improving the quality of buses and repairment.