A procedure for public transit OD matrix generation using smart card transaction data

Most fare collection systems are initially installed as single-purpose devices which are only used for collecting fare; however, many transit planners consider them as a rich source of data required for studying the passengers\u27 trip trends. Although, usually, there is no transaction made at the destination stop, making some assumptions can help us infer the destination. In this study, we present an integrated procedure that can generate origin–destination matrices and passenger load profiles as essential tools for public transport planning processes. Moreover, this procedure can be used to detect and analyze trips that include transfers. In an attempt to employ the proposed algorithm in the Tehran bus rapid transit network, 52% of the transactions could be used to trace the trips in an origin–destination format. The trips that include transfers are recognized and analyzed further. Our detailed results of the method application indicate that the proposed algorithm is a productive and economical public transport planning method

Hybrid Method for Bus Network Design with High Seasonal Demand Variation

Seasonal demand variation is a somewhat neglected aspect of transit demand. Many cities worldwide experience seasonal variation of passenger demand that deserves not only changes of service frequency, but also and often more importantly, changes in the transit network of routes. Literature dealing with optimal bus networks shows that it is an non polynomial (NP)-hard complexity problem with the demand being the most important variable affecting the network. Thus, the bus network of concern should be robust from the optimization perspective when a new demand arises. This paper introduces a model and a solution algorithm to take into account the variation of passenger demand in the design of bus networks. The solution algorithm developed is a hybrid method that optimizes the design of a bus network at the route and network levels. The model was applied to the city of Mashhad in Iran with a population of over 3.2 million and 20 million visitors annually. The results are promising and demonstrate how to determine the best single network of bus routes to suit variable passenger demand

Designing large-scale bus network with seasonal variations of demand

Creating a bus network that covers passenger demand conveniently is an important ingredient of the transit operations planning process. Certainly determination of optimal bus network is highly sensitive to any change of demand, thus it is desirable not to consider average or estimated figures, but to take into account prudently the variations of the demand. Many cities worldwide experience seasonal demand variations which naturally have impact on the convenience and optimality of the transit service. That is, the bus network should provide convenient service across all seasons. This issue, addressed in this work, has not been thoroughly dealt with neither in practice nor in the literature. Analyzing seasonal transit demand variations increases further the computational complexity of the bus-network design problem which is known as a NP-hard problem. A solution procedure using genetic algorithm efficiently, with a defined objective-function to attain the optimization, is proposed to solve this cumbersome problem. The method developed is applied to two benchmarked networks and to a case study, to the city of Mashhad in Iran with over 3.2 million residents and 20 million visitors annually. The case study, characterized by a significant seasonal demand variation, demonstrates how to find the best single network of bus routes to suit the fluctuations of the annual passenger demand. The results of comparing the proposed algorithm to previously developed algorithms show that the new development outperforms the other methods between 1% and 9% in terms of the objective function values