With skip-stop rail transit operation, transit agencies can reduce their operating costs and fleet size and passengers can experience reduced in-transit travel times without extra track and technological improvement. However, since skip-stop operation does not serve all stations, passengers for certain origins-destinations could experience increased access time, waiting time, total travel time, and/or transfer. Only when the stopping and skipping stations are carefully coordinated can skipstop service benefit passengers and transit agencies. This research developed a mathematical model using a Genetic Algorithm that coordinated the stopping and skipping stations for skip-stop rail operation. Using the flexibility of a Genetic Algorithm, this model included many realistic conditions, such as different access modes, different stopping scenarios, different collision constraints, and different objective functions. Passengers were put into three types and nine groups depending on their origin-destination pairs and the station and transferchoices. Four types of collision constraints were developed depending on the skipstop strategy. For this research, Seoul Metro system Line No. 4 was used as an example. With skipstop operation, total travel time became about 17–20 percent shorter than with original all-stop operation, depending on the stopping constraints. In-vehicle travel time became about 20–26 percent shorter due to skipping stations, although waiting, transfer, and additional access times increased by 24–38 percent.

Although demand responsive feeder bus operation is possible with human-driven vehicles, it has not been very popular and is mostly available as a special service because of its high operating costs due to intensive labor costs as well as requirement for advanced real-time information technology and complicated operation. However, once automated vehicles become available, small-sized flexible door-to-door feeder bus operation will become more realistic, so preparing for such automated flexible feeder services is necessary to take advantage of the rapid improvement of automated vehicle technology. Therefore, in this research, an algorithm for optimal flexible feeder bus routing, which considers relocation of buses for multiple stations and trains, was developed using a simulated annealing algorithm for future automated vehicle operation. An example was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled relocation of buses when the optimal bus routings were not feasible using the buses available at certain stations. Furthermore, the developed algorithm limited the maximum degree of circuity for each passenger while minimizing the total cost, including total vehicle operating costs and total passenger in-vehicle travel time costs.

Connected vehicle (CV) technology allows developing next-generation transit signal priority (TSP) that is based on cooperation between signals (change signal timing) and buses (alter speed). In this study, a CV-based TSP that was previously developed and evaluated through a computer simulation was tested in the field. The field experiment at the Smart Road testbed at the Virginia Tech Transportation Institute (Blacksburg, Virginia) validated the feasibility of CV-based TSP and evaluated its performance. The proposed TSP algorithm properly delivered TSP green times to buses with a 100% success rate. In addition, it reduced delays—between 32 and 75%—for a bus traveling approximately $72.4\mathrm{km}/\mathrm{h}$ ($45\mathrm{mi}/\mathrm{h}$ ) and a traffic signal with a 90-s cycle length with 30-s green time. Moreover, the field experiment showed that regular and differential global positioning system (GPS) devices demonstrate no statistically significant difference in performance. This finding could facilitate large-scale implementation of the TSP logic based on connected vehicle technology (TSPCV) because the regular GPS devices are readily available and much cheaper than differential GPS devices.

In recent years, express bus service has come into the spotlight by overcoming slow bus operating speeds while maintaining its accessibility when it operates with local bus services. This study developed an optimal limited-stop bus routes selection (LSBRS) guideline as a scenario-based analysis and compared it with case study results. Smart card data and a genetic algorithm (GA) were used to develop the model with different scenarios. Then, total travel time savings as a result of implementing limited-stop bus service generated by the GA model were computed. The effectiveness of each factor was verified by multiple regression analysis, and the LSBRS methodology was determined. This methodology was applied to Suwon, Korea, as a case study. As a result, travel time savings were estimated to be 9.0–19.0%. The ranking of the total travel time savings proposed by the LSBRS methodology presented a similar tendency with that of the case-study analysis.

This research developed a geographical presentation method to show which areas of the city need to be improved in terms of the directness of transit service using two measures, Degree of Competitiveness and Degree of Circuity, which were developed in the first author’s previous research. In this research, the directness of transit networks in five cities in Korea was analyzed and compared as an example and demonstrated geographically. The results show that although more populated cities are likely to provide more direct transit service than less populated cities, population density affects the transit network directness more. Also, this research showed that there is a strong relationship between transit network directness and transit ridership. As a result of this research, transit agencies can have a better visual understanding of their transit network directness and can improve transit network configuration where transit network directness is poor.