With the development of the Shanghai International Shipping Center, the diversity of vehicle types on the highways and arterial roads near Yangshan port is continually increasing. Within such a container port corridor, large container trucks are primarily utilized for mainline transportation. Their larger size and significant inertia would increase psychological pressure on sedan drivers, and elevate their behavior risk. To investigate the effects of container trucks on drivers’ visual characteristics and driving behavior as well as to predict driving risk, firstly, this research conducted field tests in four scenarios surrounding the port. Visual characteristics and behavior data of sedan drivers were collected. Secondly, a “Visual‐behavior” chain model was established. The relationship between drivers’ visual characteristics, driving behavior, and driving risk was illustrated from the perspective of time‐series behavior patterns. Thirdly, three driving risk prediction models were built with Autoregressive Integrated Moving Average (ARIMA), Long Short‐Term Memory (LSTM), and ARIMA‐LSTM. The results indicate that the ARIMA‐LSTM model shows the most effective prediction performance. This research provides a field‐data comparative analysis of the driving risks influenced by a high proportion of container trucks. The findings contribute to understanding the unique mixed traffic visual environment around large‐scale container ports.

The principle of system resilience is its ability to withstand disruptions and maintain an equilibrium state. In urban network systems, adaptive traffic signal control (ATSC) has been an effective countermeasure to mitigate traffic flow disturbance and improve resilience. This research has explored the usage of a decentralised advantage actor‐critic (a2c) algorithm‐based ATSC in mitigating disruptions, particularly nonrecurring congestion caused by car accidents. A reward function has also been proposed, combining deduced resilience metric, safety indicator time to collision (TTC) and system performance. A virtual simulation environment was created using simulation of urban mobility (SUMO) to facilitate the evaluation of the proposed approach. In the grid simulation environment, an overall 5.8% improvement is achieved, exceeding benchmark algorithms in three metrics, especially performance with a margin of over 5.2%. Robustness against different levels of car accidents are proven as well. Further evaluation is also implemented based on a real‐world case study and demonstrates an improvement of 20.08%, highlighting the correlation of proposed method’s efficiency on the traffic flow rate and road structure.

As an on‐demand mobility service, parking reservations can greatly alleviate the issue of parking challenges. There are currently three primary strategies for parking reservation: first‐come‐first‐served, permit reservation, and auction. In contrast to the first‐come‐first‐served and permit‐reserved strategies, the auction strategy uses dynamic pricing to allocate parking supplies efficiently based on the auction, which attracts more scholars for the research. However, parking reservations based on the auction process may have an inequity issue because drivers’ age, gender, income level, and location of residence fluctuate. This inequity may limit the growth of reserved parking by influencing parking drivers’ acceptance of reserved parking. But currently, very few scholars focus on the issue of reserved parking equity, and even fewer measure this nebulous and personal issue. In consideration of this, the Lorenz curve of parking reservation and the vertical equity index of parking reservation are proposed in this paper along with the calculation method for the index, which enables the problem of reserved parking vertical equity to be visualized and made concrete. The numerical experimental method is used to analyze the vertical equity of drivers with varying income levels, utilizing the Vickrey–Clarke–Groves (VCG) auction process as an example. According to the research, loss‐averse drivers are more than gain‐neutral and gain‐seeking drivers when the income levels of the drivers using reserved parking are the same. With the increasing number of high‐income drivers involved in the parking reservation, medium to low‐income drivers would lose their chances of successful reservations because of their uncompetitive bid price which leads to inequity issues when the number is less than the number of parking spaces. In contrast, the vertical equity index changes more for gain‐seeking drivers while being generally steady when loss‐averse and gain‐neutral drivers participate. For instance, when the profit and loss coefficient is 3 and 15% of drivers with high‐income levels use the parking reservation platform, the vertical equity index rises from 0.09128 to 0.45434. The reference price has a moderating influence on the vertical equity index when the number of driver participants at high‐income levels remains constant. In general, within a reasonable range, the higher the reference price, the more equitable the parking reservation procedure and the lower the vertical equity index.

Air taxis, a core service within urban air mobility (UAM), have the potential to enhance user satisfaction and address societal challenges such as traffic congestion and environmental pollution. However, the success of this service is often hindered by various concerns. To ensure successful implementation, we investigate the factors influencing public acceptance of air taxis. This study distinguishes itself from previous research in three key aspects. First, it introduces a novel classification of the factors into individual and societal dimensions. Second, it is among the first to apply a value‐based adoption model to understand the intention to adopt air taxis, including UAM. Third, it uniquely considers the Korean perspective, unlike most existing studies that focus on Western cultural contexts. To identify the consumers’ perceptions, we conducted interviews with experts and surveyed a sample of 1,000 members of the general public in Korea. Our findings suggest that perceived value for society, as well as perceived value for individual users, significantly influences adoption intention. We discuss both academic insights and practical implications for policy and industry, supporting the commercialization of Korean UAM (K‐UAM) promoted by the Korean government.

The urban rail transit network has gradually realized grid operation with the increase in the coverage rate. Therefore, the stopping schemes in accordance with the trend of the passenger flow are more conducive to improving the attractiveness of the rail transit and improving the sharing rate of the urban public transit. Traditional data from a single source may not be sufficient to describe the overall trend of the passenger flow in a period of time, and the error is possible in the case of insufficient data. Based on the multisource data, the spatial weight function is introduced to fuse the point of interest data and real estate information data, from which one obtains the residential index and office index, and the cluster analysis is conducted to obtain the potential stop scheme. Then, the optimization model of the train operation plan is established, aiming at minimizing the passenger travel time and the generalized system cost, and is constrained by a series of driving conditions. Compared with the single data source, multisource data can better reflect passenger flow trends and land use characteristics. Compared with the traditional all‐station stopping scheme, a reasonable setting of crossing stations and running express‐local trains can better satisfy the demands of the passenger flow. Finally, the optimization of Changchun rail Transit Line 1 shows that the model can reduce the travel time of passengers and the operating cost of the rail transit company and improve the quality of service, so as to achieve a win‐win situation between passengers and the rail transit company.

Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

Transit-oriented development is described as a geographic unit with multicircle structures. Most studies have analysed the impact of the built environment within station catchment areas on metro passenger flows from a macro perspective and have lacked analysis of the circle heterogeneity. Few relevant studies have independently investigated the impact of the built environment on the passenger flow in each circle and indeed neglected the systematic interaction between inner circles and circles in the TOD area. In this study, the 800 m buffer from the station was equally divided into four circles. Based on the gravity model, the representative built environment features around the metro stations on both sides were extracted using the block attention module (BAM). Subsequently, Shapley Additive exPlanation (SHAP) was used to explore the influence of different built environment variables on passenger flow at each circle between the origin and destination stations. The results indicate the following: (1) the station-to-station passenger flow is significantly affected by the availability of transfers and the distance between the origin and destination stations; (2) the impact of different built environments on ridership significantly varies within different circles; and (3) the built environment has a similar impact on average daily passenger flow on both sides. Therefore, this study proposes strategies to optimize the metro passenger flow by developing different land use in different circles and updating the urban spatial structure.

This study proposes random-parameters multinomial logit models, with heterogeneity in means and variances, to explore the differences in the factors influencing injury severities of drivers involved in different types of two-vehicle crashes. The models are verified using crash data from the United Kingdom (UK) over three years (2016–2018). Three types of crashes are separately identified (car-car, car-truck, and truck-truck crashes). In this study, a wide variety of potential variables, including the driver, vehicle, road, and environmental characteristics, are considered, with two possible injury-severity outcomes: severe and slight injury. The results show that unobserved heterogeneity existed for young drivers in both car-car and truck-truck crash models and the 30 mph speed limit in the three separate models. Remarkably variations are observed in crashes involving different types of vehicles. The driver’s age and gender, speeding, sideswipes, presence of junctions, weekdays, unlit, and weather conditions significantly impact driver-injury severities in various types of vehicle crashes. These findings are expected to help policymakers seek to improve highway safety and implement proper safety countermeasures.