A Robust Optimization Approach to Public Transit Mobile Real-time Information

  • Weimin Ma Tongji University
  • Nannan Lin Tongji University
  • Xiaoxuan Chen University of Wisconsin-Madison
  • Wenfen Zhang Wuhan University of Technology
Keywords: public transit, mobile app, real-time information, robust optimization


In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and learn about the expected arrival times of their transit vehicles. Previous studies show that provision of accurate real-time bus information is vital to passengers for reducing their anxieties and wait times at bus stops. Inadequate and/or inaccurate real-time information not only confuses passengers but also reinforces the bad image of public transit. However, almost all methods of real-time information optimization are aimed at predicting bus arrival or travel times. In order to make up for the lack of information accuracy, this paper proposes a new approach to optimize mobile real-time information for each transit route based on robust linear optimization. An error estimation is added to current bus arrival time information as a new element of mobile bus applications. The proof process of the robust optimization model is also presented in this paper. In the end, the model is tested on two comparable bus routes in Shanghai. The real-time information for these two routes was obtained from Shanghai Bus, a mobile application used in  Shanghai City. The test results reflect the validity, disadvantages, and risk costs of the model.


. Dziekan K, Kottenhoff K.(2007). Dynamic at-stop real-time information displays for public transport: effects on customers[J]. Transportation Research Part A: Policy and Practice 41(6): 489-501.

. Watkins K E, Ferris B, Borning A, et al.(2011). Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders[J]. Transportation Research Part A: Policy and Practice 45(8): 839-848.

. Grotenhuis J W, Wiegmans B W, Rietveld P.(2007). The desired quality of integrated multimodal travel information in public transport: Customer needs for time and effort savings[J]. Transport Policy 14(1): 27-38.

. Newell G F.(1971). Dispatching policies for a transportation route[J]. Transportation Science 5(1): 91-105.

. Osuna E E, Newell G F.(1972). Control strategies for an idealized public transportation system[J]. Transportation Science 6(1): 52-72.

. Newell G F.(1973). Scheduling, location, transportation, and continuum mechanics: some simple approximations to optimization problems[J]. SIAM Journal on Applied Mathematics 25(3): 346-360.

. Wirasinghe S C.(1900) Re-examination of Newell's Dispatching Policy and Extension to a Public Bus Route with Many to Many Time-Varying Demand[C]. International Symposium on Transportation and Traffic Theory 11th.

. Wirasinghe S C.(2003). Initial planning for an urban transit systems[J]. Advanced Modeling for Transit Operations and Service Planning.

. Ceder A, Stern H I.(1984). Optimal transit timetables for a fixed vehicle fleet[C].Proc. 10th International Symp on Transportation and Traffic Theory. 331-355.

. Ceder A.(1991). A procedure to adjust transit trip departure times through minimizing the maximum headway [J]. Computers & operations research 18(5): 417-431.

. Ceder A.(2001). Bus timetables with even passenger loads as opposed to even headways[J]. Transportation Research Record: Journal of the Transportation Research Board 1760(1): 3-9.

. Ceder A.(2011). Optimal multi-vehicle type transit timetabling and vehicle scheduling[J]. Procedia-Social and Behavioral Sciences 20: 19-30.

. Ceder A.(2011). Public-transport vehicle scheduling with multi vehicle type[J]. Transportation Research Part C: Emerging Technologies 19(3): 485-497.

. De Palma A, Lindsey R.(2001). Optimal timetables for public transportation[J]. Transportation Research Part B: Methodological 35(8): 789-813.

. Eberlein X J, Wilson N H M, Barnhart C, et al.(1998). The real-time deadheading problem in transit operations control[J]. Transportation Research Part B: Methodological 32(2): 77-100.

. Nielsen O A, Frederiksen R D.(2006). Optimisation of timetable-based, stochastic transit assignment models based on MSA[J]. Annals of Operations Research 144(1): 263-285.

. Zhao F, Zeng X.(2008). Optimization of transit route network, vehicle headways and timetables for large-scale transit networks[J]. European Journal of Operational Research 186(2): 841-855.

. Yan Y, Meng Q, Wang S, et al.(2012). Robust optimization model of schedule design for a fixed bus route[J]. Transportation Research Part C: Emerging Technologies 25: 113-121.

. Liu Z, Yan Y, Qu X, et al.(2013). Bus stop-skipping scheme with random travel time[J]. Transportation Research Part C: Emerging Technologies 35: 46-56.

. Mesa J A, Ortega F A, Pozo M A.(2014). Locating optimal timetables and vehicle schedules in a transit line[J]. Annals of Operations Research 222(1): 439-455.

. Bertsimas D, Sim M.(2004). The price of robustness[J]. Operations research 52(1): 35-53.

. Linhares A.(1998). Preying on optima: A predatory search strategy for combinatorial problems[C]. Systems, Man, and Cybernetics 1998: IEEE International Conference 3: 2974-2978.

. Chen M, Liu X, Xia J.(2005). Dynamic prediction method with schedule recovery impact for bus arrival time[J]. Transportation Research Record: Journal of the Transportation Research Board 2005 (1923): 208-217.

How to Cite
Ma W, Lin N, Chen X, Zhang W. A Robust Optimization Approach to Public Transit Mobile Real-time Information. PROMET [Internet]. 2018Sep.7 [cited 2020Oct.29];30(5):501-12. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/2609