Expressway traffic information is important for guiding driving routes and alleviating traffic congestion. However, the current research on expressway guidance information focuses on existing expressways. In this study, strategies for providing expressway guidance information under reconstruction and expansion scenarios are investigated. Multiple factors of expressway reconstruction and expansion, such as the length of construction areas and the number of lanes occupied by construction areas, are extracted. A panel latent class logit model considering individual heterogeneity is established to fit the survey data collected by 825 respondents. The results show that the proposed panel latent class logit model fits the data best, and the studied drivers could be categorized into three classes, i.e., the information provision time-sensitive class, the information promotion detour class, and the information suppression detour class. The research results can support expressway operators in designing appropriate traffic information provision strategies, providing personalized guidance to drivers, and ensuring the safe operation of expressways in construction areas.

Influence of Expressway Construction Area Information on Drivers’ Route Choice Behaviours

Considering the limitations of existing point-to-point guidance in airport terminals, this paper introduces an interactive chain-based guidance which can provide navigation incorporating passengers' activity chains and congestion information. To evaluate the efficiency of the interactive chain-based guidance, we developed a multi-agent simulation model incorporating passengers' chain-based decision (MAAC), the model comprises four modules environment design, trajectory generation, satisfaction evaluation and activity chain updates. A unique survey integrating virtual terminal is developed to collect passengers' behavioural data in Beijing Capital International Airport. The model is implemented on the platform of MATSim, and the results indicate a mean absolute percentage error of 20.44%. The interactive chain-based guidance can lead to a 28.29% reduction in corridor congestion, a 29.24% decrease in crowd density, and a 12.52% improvement in average satisfaction score. According to the results, the interactive chain-based guidance is efficient in improving passengers' satisfaction and organizing the passenger flow for hub airports. 

Passenger flow simulation of hub airports incorporating interactive chain-based guidance

To unveil the influencing factors of drivers’ curbside charging choice behavior and provide valuable insights for the deployment of curbside charging infrastructure, a stated preference survey involving 402 respondents was conducted in Chongqing, China. A correlated mixed logit model was then employed to fit the survey data. The proposed model of drivers’ curbside charging choice behavior focuses on three factors: personal attributes, vehicle characteristics, and charging facility attributes. Findings suggest that individuals with higher monthly incomes, a positive attitude, and older age are more inclined to choose curbside chargers, while public institution employees and younger individuals are less likely to do so. In addition, factors such as lower remaining range, longer parking durations, proximity to residential areas, and the availability of fast chargers positively influence the likelihood of selecting a curbside charger. Moreover, unobserved heterogeneity factors, such as monthly income, parking duration, and pricing, along with their interactions, further underscore the complexity of the factors influencing curbside charger choice behavior. 

In-Depth Exploration of Factors Influencing Drivers’ Choice of Curbside Charges

Empirical Study on Influencing Factor of Park&amp;Ride Usage in Tourist Areas Using a Mobile Application

The active traffic management system disseminates traffic information to drivers to guide their route choice, so as to alleviate traffic congestion. Most drivers are highly dependent on mobile navigation APP, and the display form of mobile navigation information is of great importance. In order to improve drivers’ compliance with guidance information, this research accommodates for information that disseminate time attribute (when to provide the traffic information) and display format attribute (voice navigation or not, flat map or three-dimensional [3D] map) and also explores the interaction effects between individual and trip characteristics. A revealed preference (RP) and stated preference (SP) survey consisting of 831 respondents is conducted in Nanjing, China. Then three kinds of panel logit model are established to fit the survey data. According to the model results, it is found that drivers with aggressive style are more sensitive to travel time, and drivers who are familiar with the road network are more sensitive to the 3D map with voice navigation format. The results of parameter estimation are also used for the trade-off analysis of various exogenous variables. The findings of this study provide useful insights for the development of mobile navigation APPs, personalized path recommendation and congestion pricing strategies. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/itr2.12218

Influence of smartphone traffic application information on drivers’ route choice behaviours

The article presents the results of combining 4 different types of neural network learning: evolutionary, reinforcing, deep and extrapolating. The last two are used as the primary method for reducing the dimension of the input signal of the system and simplifying the process of its training in terms of computational complexity.In the presented work, the neural network structure of the control device of the modeled system is formed in the course of the evolutionary process, taking into account the currently known structural and developmental features of self-learning systems that take place in living nature. This method of constructing it makes it possible to bypass the specific limitations of models created on the basis of recombination of already known topologies of neural networks.

Neuroevolutionary reinforcing learning of neural networks

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Influence of smartphone traffic application information on drivers’ route choice behaviours

Neuroevolutionary reinforcing learning of neural networks