Optimal design of promotion based demand management strategies in urban rail systems
place - urban, mode - rail, planning - travel demand management, planning - service improvement, technology - management information systems, operations - capacity, operations - performance
Optimal promotion design, Demand management, Urban rail systems, Smart card data
Travel demand management (TDM) is used for managing congestion in urban areas. While TDM is well studied for car traffic, its application in transit is still emerging. Well-structured transit TDM approaches can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. However, transit systems are complex and the design of a TDM scheme, deciding when, where, and how much discount or surcharge is implemented, is not trivial. The paper proposes a general framework for the optimal design of promotion based TDM strategies in urban rail systems. The framework consists of two major components: network performance and optimization. The network performance model updates the origin-destination (OD) demand based on the response to the promotion strategy, assigns it to the network, and estimates performance metrics. The optimization model allocates resources to maximize promotion performance in a cost effective way by better targeting users whose behavioral response to the promotion improves system performance. The optimal design of promotion strategies is facilitated by the availability of smart card (automated fare collection, AFC) data. The proposed approach is demonstrated with data from a busy urban rail system. The results illustrate the value of the method, compare the effectiveness of different strategies, and highlight the limits of the effectiveness of such strategies.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Ma, Z., & Koutsopoulos, H.N. (2019). Optimal design of promotion based demand management strategies in urban rail systems. Transportation Research Part C: Emerging Technologies, Vol. 109, pp. 155-173.