Travel mode selection is a crucial aspect of traffic distribution and forecasting in a comprehensive transportation system, which has significant implications for resource allocation and optimal management. As commuters are the main part of urban travel, studying the factors that affect their choice of transport mode plays a crucial role in urban traffic management and planning. Based on public transport operation data, a travel chain is created by identifying boarding stations, alighting stations, and transfer behaviors, and includes detailed travel information. The regression and correlation coefficients of departures and arrivals at stations are confirmed to be 0.98 and 0.92 in the presented data, indicating the viability of the recognition method. Then, multiple travel modes are identified based on the origin and destination, and the proportion of mode selection is determined by the actual travel chain. Using maximum likelihood estimation (MLS) and NLOGIT software, the random parameter logit (RPL) mode is used to estimate the relationship between travel mode selection and characteristic variables such as travel time, distance, cost, comfort, walking distance, and waiting time. The results indicate that walking distance, travel distance, and comfort have a greater influence on travel choice, and that walking distance is a random parameter with a normal distribution, reflecting the diversity of commuters. In addition, this paper discusses the influence degree of the change of characteristic variables of a transport mode on the choice between it and other modes. These results can be used as reference for relevant departments to make measures to improve the overall efficiency of the urban transportation system.