Traffic signal control at signalised intersections in urban cities has significant impacts on traffic performance. Connected-vehicle data, which is wirelessly transmitted by individual vehicles to roadside units, produces a clearer picture of traffic. Using this source of new data, an adaptive signal-control algorithm was developed with the aim of reducing traffic delays at intersections. A certain type of traffic-state information (elapsed time, vehicle stops or queue length) was first integrated for each traffic phase to reveal current traffic conditions. Phase sequence and duration were then adjusted, with the maximum and minimum restrictions of green time. The proposed algorithm was tested in an intersection simulated in the Vissim software package and the algorithm based on elapsed time was found to perform best. A comparison of the elapsed-time-based control scheme and the optimal fixed control scheme indicated that the former scheme outperformed the latter, with lower average vehicle delays in each traffic demand case even under low connected-vehicle penetration rates and with less variance in average delay for each traffic movement in most scenarios. In summary, the proposed signal-control algorithm could improve the operation of isolated intersections efficiently and serve all traffic movements in a more impartial way.

Understanding passengers’ trip activity, especially the distribution along the metro line, is important to rail transit operation and management. Using large volumes of mobile phone user data, we derive the trip information of rail passengers. In this study, according to the generation principle and structure of the phone data, the feature of passengers’ phone data is analyzed and summarized. Based on the feature analysis, a phone-based identification method is proposed to obtain the passenger’s trip information on one metro line. By using the trip data, we analyze the distribution of a line’s passengers from the temporary and spatial dimension. The paper demonstrates the utilization of phone data in the metro system and the method we proposed contributes to the further study of passengers’ trips outside the metro.