When demand for transportation is low or highly variable, traditional public bus services tend to lose their efficiency and typically frustrate (potential) passengers. In the literature, a large number of demand-responsive systems, that promise improved flexibility, have therefore been developed. At present, however, a comprehensive survey of these systems is lacking. In this paper, we fill this gap by presenting a unifying framework that classifies all demand-responsive public bus systems. The classification is mainly based on three degrees of responsiveness: dynamic online, dynamic offline, and static. For each system we discuss the specific optimization problem modeled, whether realistic data is considered, and the size of the instances used for testing. Where possible, we try to draw conclusions on the current state of the literature and try to identify potential avenues for future research. Different tables are included to structure and summarize the information of all papers. 

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A survey on demand-responsive public bus systems

Feeder services are public transit services that transport people from a low demand, typically suburban, area to a high demand area, such as a transportation hub or a city. Here, passengers continue their journey using traditional forms of public transport. On the one hand, on-demand feeder services have been a topic of discussion in a number of recent studies, since these services can serve the demand efficiently. On the other hand, traditional feeder services with predetermined routes and timetables provide predictability and easier cost control. In this paper, a demand-responsive feeder service is considered, which combines positive characteristics of both traditional services as well as on-demand-only services. This feeder service has mandatory bus stops which are always serviced, as well as optional bus stops which are only serviced when there is demand for transportation nearby. To optimize the performance of this feeder service, a large neighborhood search heuristic is developed. Experimental results on 14 benchmark instances illustrate that the LNS algorithm obtains solutions with an average gap of 1% or less compared to the optimal solution, within 1s of runtime. Larger instances can also be solved, typically in less than 60s. The results also show that the demand-responsive feeder service generally outperforms a traditional service in terms of service quality, often by more than 60%.

A large neighborhood search algorithm to optimize a demand-responsive feeder service

Public transport for smart cities: Recent innovations and future challenges

Customized bus (CB) provides convenient door-to-door transportation services to passengers through a subscription-based model. To ease the problem of insufficient taxis and private hire services at large transport terminals, we propose and evaluate a customized CB service for railway stations and airports in China. The proposed CB problem considers a routing problem with time-dependent travel time and late-customers. A periodic optimization approach is adopted to periodically collect passenger demands, optimize customized bus routes for the period, and re-optimize the routes to cater to late-coming customers. The static problem for each period is modeled as a time-dependent customized bus routing problem with time windows and release date (TDCBRPTW-R), which is solved with a hybrid meta-heuristic algorithm combining adaptive large neighborhood search (ALNS) with variable neighborhood descent (VND). An acceleration method is developed for insertion feasibility check for the TDCBRPTW-R. The stability and accuracy of the proposed algorithm is evaluated in the numerical experiments derived from Solomon’s data set. For the periodic re-optimization procedure, four methods are proposed to handle late customers according to different application scenarios. The re-optimization procedure is tested on the road network in Chengdu with passenger data provided by Didi Chuxing. The experiments demonstrate that a loose time window allows the service to serve more passengers and reduces operational costs. Lastly, the experiments also show the effectiveness of the proposed methods in handling late customers. • A periodic customized bus service for large transport terminals in China. • A static MIP model for the static problem with time-dependent travel time. • A hybrid meta-heuristic algorithm with an accelerated feasibility check method. • Dynamic optimization approaches to handle late customers. • Comprehensive experiments on both the static problem and the dynamic problem. 

Time-dependent customized bus routing problem of large transport terminals considering the impact of late passengers

The real-time on-demand bus routing problem: The cost of dynamic requests

With the rise of smart cities, relevant passenger data can be collected to improve the quality of transport services. In this article, a demand‐responsive feeder service is presented. A feeder service transports passengers from a low‐demand area, like a suburban area, to a transportation hub, like a city center. The feeder service modeled in this article considers two sets of bus stops: mandatory stops and optional stops. Mandatory stops are always visited by a bus, while optional stops are only visited when a client nearby makes a request for transportation. This gives the service both flexibility and some predictability. To optimize the performance of the service, mathematical modeling techniques to improve the model's runtime are developed. It is concluded that a combination of column generation and the separation of sub‐tour elimination constraints decreases the computing time of small and midsize instances significantly.

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A column generation algorithm for the demand‐responsive feeder service with mandatory and optional, clustered bus‐stops

With the rise of smart cities in the near future, it will be possible to collect relevant data from passengers in order to improve the quality of transport services. In this paper, a mathematical model and algorithm are developed to plan the trips of the buses in a demand-responsive feeder service. A feeder service transports passengers from a lowdemand area, like a sub-urban area, to a transportation hub, like a city center. The feeder service modeled in this paper considers two sets of bus stops: mandatory stops and optional stops. Mandatory stops are always visited by a bus, while optional stops are only visited when a client nearby makes a request for transportation. Passengers are assigned to a bus stop within walking distance. This in turn, gives the service both exibility through the changing timetables and routes of the buses and some predictability due to the mandatory stops. To optimize the performance of the service, mathematical modeling techniques to improve the model's runtime are developed. It is concluded that a combination of column generation and the separation of sub-tour elimination constraints decreases the computing time of small and midsize instances significantly.

Bryan David Galarza Montenegro

Papers

A survey on demand-responsive public bus systems

A large neighborhood search algorithm to optimize a demand-responsive feeder service

A column generation algorithm for the demand‐responsive feeder service with mandatory and optional, clustered bus‐stops

A demand-responsive feeder service with mandatory and optional, clustered bus-stops