Editorial

In recent years, there has been rapid progress in the development of information and communication technologies (ICT), leading to more powerful applications. The transportation industry also benefits from the advancement of the technologies. The information that was previously unavailablemay now be captured through numerous sensing devices for analysis and enhancements of transportation systems. The high-dimensional information collected from multiple data sources can be integrated for greater potential values. The ultimate goals are to make transportation systems smarter, safer, better coordinated, and more efficient, reliable, resilient and sustainable. There have been various prevalent applications enabled by advanced ICT, including real-time traffic signal control (e.g. Feng et al. 2015; Ramezani et al. 2017), traffic or travel time prediction (e.g. Lu and Dong 2017; Li et al., 2017; Min and Wynter 2011; Vlahogianni et al. 2014; Wang et al. 2016; Yildirimoglu and Geroliminis 2013), advanced public transit systems (e.g. Nuzzolo and Comi 2016), and inference of transportation-related activities using smart-card data (e.g. Legara andMonterola 2017; Ma et al. 2013; Medina 2016; Pelletier et al. 2011; Yang et al. 2017) to name a few. This special issue of Transportmetrica B: TransportDynamics aims to address four key aspects of the timely topic of ‘smart transportation and analytics’ and present the recent advance of research in the area. One important aspect that enables smart transportationmanagement is the control of traffic lights, which is crucial for the efficiency of transportation systems. The first paper included in this special issue was contributed by Chen et al. and titled ‘Effects of Traffic Lights for Manhattan-like Urban Traffic Network in Intelligent Transportation Systems.’ The authors consider four traffic light rules at an intersection – clockwise, anticlockwise, 8-like, and reverse 8-like – and adopted a simulation approach to study their performances on a Manhattan-like network in the presence of route guidance information. They also examine other factors that can affect the road traffic systems, including the number of traffic lights and their signal period. From their computational experiments, they conclude that a combination of the anticlockwise rule and the congestion coefficient feedback strategy for route guidance results in the best performance. While an intelligent transportation control system can significantly enhance the efficiency of a transportation system, there can be unexpected and unavoidable events that impose burdens on the management of transportation operations. In particular, public transit often suffers from daily transportation disruptions, such as accidents and traffic congestion. Public transportation operators, once faced such disruptions, shall make a prompt and effective response to recover services. The second paper contributed by Lai and Leung, titled ‘Real-time Rescheduling and Disruption Management for Public Transit’, presents an application of fast and automated recovery of tram services. They utilize both historical and real-time locational information about the trams in their mixed-integer programmingmodel for rescheduling trams andmotormen simultaneously under a rolling-horizon framework. Their proposed methodology was tested in a simulation environment of the tram network based on historical locational data and was shown to be successful in reducing motormen overtime and meal-break delays without harming service quality. In addition to maintaining good-quality transit services, public transportation operators may also wish tomake real-time travel information available at transit stops or accessible from the Internet such that passengers canplan their travel activities better. The thirdpaperbyWepulanon etal., titled ‘ARealtime Bus Arrival Time Information SystemUsing Crowdsourced Smartphone Data: a Novel Framework and Simulation Experiments’, presents an innovative application that uses crowdsourced smartphone