Trip Chaining Model with Classification and Optimization Parameters

In order to model the complex requirements of users travelling in an urban environment, the relevant parameters for creating activity chains have to be identified. In this study, travel related parameters were collected and grouped into two main types: classification parameters and optimization parameters. In the case of optimization parameters, further grouping was performed where general and comfort parameters were introduced. Additionally, the possible values and data sources of the parameters were identified. A utility function was created to take into account the optimization parameters and the weights. Weights related to comfort optimization parameters were aggregated to decrease the number of required settings by the users. Finally, the features of the proposed optimization algorithm are described. With the identified parameters, aggregated weights and elaborated utility function activity chains can be optimized for users with different requirements.

A Method for Bus OD Matrix Estimation Using Multisource Data

The automated fare collection (AFC) system has gained increasing popularity among transit systems worldwide. The AFC system is usually an entry-only system that only records the serial number of the smart card and the transaction time of each use. Neither the AFC data nor the bus global positioning system (GPS) could reveal the passenger’s alighting information, namely, alighting time and station. Hence, the station-to-station origin-destination (OD) trip information cannot be obtained directly from the available data sources. To address this problem, this paper proposes a methodology that estimates the OD matrix by using smart card and GPS data. In this paper, the characteristics of the basic data sources are first analyzed, based on which the bus arrival time is generated using the density-based clustering algorithm and a time correction strategy, based on which the passenger’s boarding station is identified. The alighting stations are inferred based on the characteristics of bus trip chaining, which could identify over 80% of the alighting stations on average. Finally, the proposed methodology is verified by a comprehensive field survey in Suzhou, China, with 100% sample rate.