Assessing cyclists’ routing preferences by analyzing extensive user setting data from a bike-routing engine

Introduction Many municipalities aim to support the uptake of cycling as an environmentally friendly and healthy mode of transport. It is therefore crucial to meet the demand of cyclists when adapting road infrastructure. Previous studies researching cyclists’ route choice behavior deliver valuable insights but are constrained by laboratory conditions, limitations in the number of observations, or the observation period or relay on specific use cases. Methods The present study analyzes a dataset of over 450,000 observations of cyclists’ routing settings for the navigation of individual trips in Berlin, Germany. It therefore analyzes query data recorded in the bike-routing engine BBBike and clusters the many different user settings with regard to preferred route characteristics. Results and Conclusion Results condense the large number of routing settings into characteristic preference clusters. Compared with earlier findings, the big data approach highlights the significance of short routes, side streets and the importance of high-quality surfaces for routing choices, while cycling on dedicated facilities seems a little less important. Consequentially, providing separated cycle facilities along main roads – often the main focal point of cycle plans – should be put into the context of an integrated strategy which fulfills distinct preferences to achieve greater success. It is therefore particularly important to provide a cycle network in calm residential streets as well as catering for short, direct cycle routes.

A Stochastic User Equilibrium Formulation for the Cumulative Prospect Theory-Based Cross-Nested Logit

The cumulative prospect theory provides a better description for route choice behavior of the travelers in an uncertain road network environment. In this study, we proposed a multiclass cumulative prospect value- (CPV-) based cross-nested logit (CNL) stochastic user equilibrium (SUE) model. For this model, an equivalent variational inequality (VI) model is provided, and the existence and equivalence of the model solutions are also proved. The method of successive averages (MSA), method of successive weighted averages (MSWA), and self-regulated averaging (SRA) method are designed and compared. In addition, the proposed multiclass CPV-based CNL SUE model is also compared with the multiclass utility value- (UV-) based CNL SUE model. The results show that the path flow assigned by the multiclass CPV-based CNL SUE model is more consistent with the actual situation. The impact of different model parameters on the cumulative prospect value (CPV) is investigated.

Preserving Privacy with Federated Learning in Route Choice Behavior Modeling

While big data helps improve decision-making and model developments, it often runs into privacy concerns. An example would be retrieving drivers’ origin and destination information from smartphone navigation apps for developing a route choice behavior model. To conserve privacy, yet to take advantage of big data in navigation applications, the authors propose to apply a federated learning approach, which has shown promising application in predicting smartphone keyboard’s next word without sending text to the server. Additional benefits of using federated learning is to save on data communications, by sending model parameters instead of entire raw data, and to distribute the computational burden to each smartphone instead of to the main server. The results from real-world route navigation usage data from about 30,000 drivers over one year showed that the proposed federated learning approach was able to achieve very similar accuracy to the traditional centralized global model and yet assures privacy.

Simulating the effects of redesigned street-scale built environments on access/egress pedestrian flows to metro stations

In urban renewal processes, metro line systems are widely used to accommodate the massive traffic needs and stimulate the redevelopment of the local area. The route choice of pedestrians, emanating from or going to the metro stations, is influenced by the street-scale built environment. Many renewal processes involve the improvement of the street-level built environment and thus influence pedestrian flows. To assess the effects of urban design on pedestrian flows, this article presents the results of a simulation model of pedestrian route choice behavior around Yingkoudao metro station in the city center of Tianjin, China. Simulated pedestrian flows based on 4 scenarios of changes in street-scale built environment characteristics are compared. Results indicate that the main streets are disproportionally more affected than smaller streets. The promotion of an intensified land use mix does not lead to a high increase in the number of pedestrians who choose the involved route when traveling from/to the metro station, assuming fixed destination choice.

A Congestion Bottleneck Analysis Model for Tourist Block Street Network

The development of the tourism industry has led to increased pressure of people flow in tourist blocks. Therefore, it is critical to ease the traffic pressure in these blocks. This paper aims to identify the bottleneck links of street networks in tourist blocks to achieve the effective prevention of congestion accidents. A logit stochastic user equilibrium model combined with spatial syntax is presented to study the travelers’ route choice behavior. The nonlinear Bureau of Public Roads function is applied to calculate the time impedance of each street. A case analysis of the Chongqing Ciqikou tourist block shows that the bottleneck link has the features of high integration and a large degree of negative time impedance evolution. The research’s results are more consistent with practical circumstances because the influence of the road network topological structure on pedestrian path selection has been considered.

CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions

Emergencies have a significant impact on the passenger flow of urban rail transit. It is of great practical significance to accurately predict the urban rail transit passenger flow and carry out research on its temporal and spatial distributions under emergency conditions. Urban rail transit operating units currently use video surveillance information mainly to process emergencies and rarely use computer vision technology to analyze passenger flow information collected. Accordingly, this paper proposes a passenger flow-based temporal and spatial distribution model for urban rail transit emergencies based on the CPT. First, this paper clarifies the categories and classification of urban rail transit emergencies, analyzes the factors affecting passenger route selection, and establishes a generalized travel cost model for passengers under emergencies. Second, this paper establishes a passenger route choice behavior model for urban rail transit based on the cumulative prospect theory. Finally, taking Beijing as an example, this paper analyzes passenger travel behavior under emergencies based on multiple logistic regression models and analyzes the impact of emergencies on rail transit travel behavior. The research results show that the cumulative prospect theory can better describe the route choice behavior of rail transit passengers under emergencies than the existing models, and this model is of great significance for handling urban rail transit emergencies. The model proposed in this paper can provide a theoretical basis for the government and relevant departments to formulate traffic management measures.