The rapid development of big data and artificial intelligence (AI) is revolutionizing the automotive and transportation industries, leading to the creation of the Autonomous Modular Bus (AMB). Designed to address the key challenges of modern public transportation systems, the AMB adopts a modular dynamic assembly approach. However, existing research on the AMB predominantly focuses on operational aspects, whereas in-transit docking remains the primary obstacle to its commercial deployment. This challenge stems from the fact that current perception accuracy in autonomous vehicles is limited to the decimeter level, with insufficient capability to manage adverse weather and complex traffic conditions. To enable AMBs to achieve full-scenario autonomous driving capabilities, this paper reviews current perception technologies from three perspectives: single-vehicle single-sensor perception, multi-sensor fusion perception, and cooperative perception. It examines the characteristics of existing perception solutions and evaluates their applicability to AMB-specific requirements. Furthermore, considering the unique challenges of in-transit docking, this paper identifies and proposes four future research directions for advancing AMB perception systems as well as general autonomous driving technologies.

Within autonomous driving research, the intricate variability of the road surface is frequently overlooked, while the tire-road interactions critically impact vehicle stability. This paper comprehensively reviews traditional and emerging tire-road adhesion coefficient (TRAC) estimation methods for intelligent vehicles. We initially categorize traditional methods into cause-based and effect-based approaches, which are founded on vehicle responses and road surface characteristics, respectively. Then, we classify emerging methods into learning-based approaches and hybrid models combining physical principles with data-driven strategies. We eventually point out areas for improvement and future research directions. The proposed systematic taxonomy summarizes the independent and collaborative operations of dynamics analysis and learning methods in TRAC estimation, offering insights for further research.

The development of electric vertical takeoff and landing (eVTOL) aircrafts promotes the prosperity of Urban Air Mobility (UAM). While numerous efforts have been made on UAM, little was focusing on the integrated operation of UAM and ground transportation system. This paper thus investigated how to alleviate traffic congestion on an arterial road with a bottleneck via using eVTOL aircrafts to transfer passengers upstream the bottleneck to downstream. The location, opening and closing time of the vertiports and the transfer ratio of the passengers are optimized with the objective of minimizing the average monetary cost, which is an integrated evaluation value converted by the electricity consumption, travel time and vertiport operating cost. Based on this, an optimization model is constructed and Dividing RECTangles algorithm is adopted to solve it using the simulation module as a subroutine. Simulation results show that the proposed method is capable of reducing average monetary cost and average travel time. Moreover, due to the differences in salaries and electricity prices, the departure vertiport in U.S. case should be positioned as close to the upstream as possible, while in China it is the opposite. More importantly, we found that the successful implementation of the proposed method requires the ride-sharing service provider to arrange the travel plan in a unified manner because of the higher cost of transferred passengers. This research pioneers a novel UAM-based method to alleviate traffic congestion on an arterial road, setting the stage for the full exploitation of UAM capabilities to reduce ground traffic congestion.

Curve Speed Warning (CSW) systems assist drivers in adjusting speeds before entering a curve to improve road safety. As an essential part of CSW, the safe speed model is key in determining the speed trajectory. Current safe speed models are mostly based on the theoretical line shape of the road, which leads to the neglect of the driving differences, and it is likely to result in unreasonable speed guidance. This paper proposes a more comprehensive method to provide a safe speed trajectory in advance and enhance safety for trucks with heavy loads when approaching curve-slope sections. First, a classification model applying a random forest algorithm is developed to output the critical safe speed in a specific scenario. Second, a variable speed limit algorithm for a given path is framed, minimizing fuel and travel time consumption, and then embedded with a variable speed limit determination process. Simulation experiments are implemented based on real-world paths to verify the proposed structure. The findings indicate that our model is capable of generating speed trajectories adaptively. Additionally, experiments underscore the significant influence that the weight of the load and its center of gravity (CG) exert on the stability assessment of trucks, as we conclude that the optimal loading strategy for trucks is to reach a full load and avoid the load’s lateral offset.

Since emergency vehicles (EMVs) in urban transit systems play a crucial role in responding to time-critical events, the quick response of EMVs is essential for improving the success rate of rescue operations and minimizing property loss. Booming connected vehicle (CV) technology provides a new perspective to further enhance the effectiveness of EMV priority. Based on this CV technology, we propose a cooperative multiple EMV priority model in which the speed, acceleration, and lane changing actions of both the EMVs and surrounding ordinary vehicles (OVs) are set as decision variables. This proposed model is rigorously formulated in integer linear programming to maximize the EMV traffic efficiency and find a trade-off between the interference with normal traffic flows and the smoothness of the EMV driving trajectories. Two customized algorithms are developed to reduce the number of decision variables and constraints to obtain the better feasible solution in an acceptable computational time. A numerical experiment based on real-world data is proposed to further verify the utility and effectiveness of the aforementioned mathematical model. The customized algorithms achieve near-exact solutions with significantly faster computation compared to the benchmark solver. The robustness of the proposed model is tested with different parameter settings in the sensitivity analysis.

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Freeway on-ramps are typical bottlenecks in the freeway network due to the frequent disturbances caused by their associated merging, weaving, and lane-changing behaviors. With real-time communication and precise motion control, Connected and Autonomous Vehicles (CAVs) provide an opportunity to substantially enhance the traffic operational performance of on-ramp bottlenecks. In this paper, we propose an upper-level control strategy to coordinate the two traffic streams at on-ramp merging through proactive gap creation and platoon formation. The coordination consists of three components: 1) mainline vehicles proactively decelerate to create large merging gaps; 2) ramp vehicles form platoons before entering the main road; 3) the gaps created on the main road and the platoons formed on the ramp are coordinated with each other in terms of size, speed, and arrival time. The coordination is formulated as a constrained optimization problem, incorporating both macroscopic and microscopic traffic flow models. The model uses traffic state parameters as inputs and determines the optimal coordination plan adaptive to real-time traffic conditions. The benefits of the proposed coordination are demonstrated through an illustrative case study. Results show that the coordination is compatible with real-world implementation and can substantially improve the overall efficiency of on-ramp merging, especially under high traffic volume conditions, where recurrent traffic congestion is prevented, and merging throughput increased.

Accurate visual perception and comprehensive scene understanding are critical for the safety and reliability of autonomous vehicles (AVs). Nevertheless, the efficacy of visual perception systems can be impaired by the intricacy of road scenes, and the existing scene understanding approach may be insufficient. Consequently, this study proposes an enhanced scene understanding model to achieve precise awareness of driving situations. Recognizing the limitations posed by the oversimplification of samples in current urban scene datasets, we selected critical frames from 336000 video frames, sourced from real-world driving environments, to assemble a more complex road scene (CRS) dataset. We integrated Residual Neural Network and pyramid scene parsing network architectures and refined them through class mapping and targeted network fine-tuning. Based on the segmentation outputs and the XGBoost algorithm, we identified the driving scenarios for the ego vehicle, enabling instantaneous driving situation analysis. The predictive model also evaluated the trajectory of interactive vehicles and estimated their kinematic states. Furthermore, we have conducted a thorough evaluation of scenario complexity, integrating the features described above. The findings indicate that our model achieves a segmentation accuracy of 78.8% in CRSs, with a twofold improvement in training efficiency. We also confirmed the effectiveness of the scene understanding approach through real-world road testing in China. This research provides insight into situation awareness within CRSs, thereby enhancing the visual perception capabilities of AVs. The implications of these results are substantial for their application in autonomous driving tests and advancing decision-making and control algorithms.

A major shortcoming of the conventional car-following models is that these models only consider the current spacing and speeds of the target vehicle and its immediate leading vehicle, without taking into account prior driving actions, even for those from the same driver. In other words, the numerous prior experiences have no influence in predicting vehicular movements for the next time step. In this research, we propose a machine-learning-based data-driven methodology that is able to take advantage of the high-resolution historical traffic data in the current data-rich era, to predict vehicular movements in an accurate manner with high computational efficiency. The proposed car-following model has a simple model structure based on a fixed-radius near neighbors (FRNN) search algorithm and it can be applied to high-resolution, real-time vehicle movement prediction, modeling, and control. A comprehensive performance comparison is also conducted among the proposed car-following model, another similar data-driven model, and two conventional formula-based models. The results indicate that the FRNN algorithm-based car-following model is superior to all other three models in terms of prediction accuracy and is more computationally efficient compared to its data-driven-based counterpart. Some extensive applications of the proposed car-following model are also discussed at the end of this article.

Battery electric buses (BEBs) have been regarded as effective options for sustainable mobility while their promotion is highly affected by the total cost associated with their entire life cycle from the perspective of urban transit agencies. In this research, we develop a collaborative optimization model for the lifecycle cost of BEB system, considering both overnight and opportunity charging methods. This model aims to jointly optimize the initial capital cost and use-phase operating cost by synchronously planning the infrastructure procurement and fleet scheduling. In particular, several practical factors, such as charging pattern effect, battery downsizing benefits, and time-of-use dynamic electricity price, are considered to improve the applicability of the model. A hybrid heuristic based on the tabu search and immune genetic algorithm is customized to effectively solve the model that is reformulated as the bi-level optimization problem. A numerical case study is presented to demonstrate the model and solution method. The results indicate that the proposed optimization model can help to reduce the lifecycle cost by 7.77% and 6.64% for overnight and opportunity charging systems, respectively, compared to the conventional management strategy. Additionally, a series of simulations for sensitivity analysis are conducted to further evaluate the key parameters and compare their respective life cycle performance. The policy implications for BEB promotion are also discussed.

Transport accessibility and urban-rural connectivity are seen as critical aspects of rural economic development. In the transit network, passenger flow between urban-rural corridors demonstrates directional imbalances and low utilization of scarce resources. Freight transportation, on the other hand, lags due to poor geography, high operating costs, and scattered demand. This paper proposes a new mode of public transit that integrates passenger and freight transport, providing a carrier for logistics while compensating for the low utilization of passenger transport. In this mode, each timetabled round trip is divided into one dedicated passenger trip with high demand and one mixed-flow trip with on-demand requests. A space-time-state network is constructed considering the picking-up time window, loading/unloading service time, and electric bus energy replenishment. A mixed-integer linear programming model is developed to optimize the bus schedule that covers the travel demands and the charging requests with minimized travel costs. A Lagrangian relaxation framework with a dynamic programming algorithm and sub-gradient method is presented for problem-solving. The real-life rural-urban transport instance and a simulated network demonstrate the operation of the new mode and validate the efficiency of the proposed method. The innovative concept and the optimization framework are expected to serve as a reference for public administration to alleviate passenger and freight transportation bottlenecks in the urban-rural context.

The performance of transit signal priority (TSP) with conflicting priority requests highly depends on the serving sequence of multiple TSP requests. A series of existing methods have been developed to determine the priority level of requests. However, most of these methods focused on isolated intersections or a small number of intersections, which are not applicable to complex, dynamic and nonlinear urban traffic networks. In this regard, we propose a multi-agent TSP control method at the network level considering conflicting priority requests. Fuzzy inference is used to manage signal control. We further develop a specific control algorithm. The performance of the proposed method is verified by a case study with a sizeable traffic network with 20 intersections and 49 links. Simulation results demonstrate that the proposed method outperforms other three benchmarking methods under different traffic demands and bus departure frequencies. It is worth-noting that the improvement becomes more notable with the increase of traffic demands and the reduction of bus departure frequencies.

With the development of intelligent transportation technology, more and more traffic information can be obtained to enhance arterial coordination efficiency. A time-varying arterial coordination control program is proposed in this paper. By extracting Origin-Destination (OD) information from Automatic number plate recognition (ANPR) data, an OD-based Multipath-Band method (MP-BAND) is developed to achieve the function of the program. The proposed method breaks vehicle routing at intersections, and treats one link as an analysis unit. The method can automatically select link paths for coordination, and multiple paths can be synchronized simultaneously according to the actual traffic condition. The overlap bandwidth is introduced to capture the connectivity between paths of adjacent intersections. The PM-BAND is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical tests are conducted to evaluate the performance of MP-BAND under different traffic conditions. The results have demonstrated that MP-BAND outperformed MULTIBAND and AM-BAND in various aspects. MP-BAND can improve network performance significantly almost in all scenarios. Moreover, the intersection efficiency was improved significantly, especially the efficiency of left-turning vehicles. Also, MP-BAND can well recognize major routes and improve traffic efficiency. Compared with Synchro, MP-BAND also had superiority when flow rate is not too high. Thus, MP-BAND has a much wider applicability, and can be treated as an optimization core to achieve time-varying arterial coordination.

It has been well recognized that weather variations significantly impact cycling experiences of users. However, the weather-cycling dynamic relationship over time is not well studied in the literature. In this paper, in order to bridge this gap, we propose a Dynamic Model Averaging and Dynamic Model Selection (DMA and DMS) to reveal the characteristics of time-varying responses and the associated influencing factors for young people's shared bike trips. Without loss of generality, dynamic models with unknown observational variances are also proposed. We take New York City as an instance and analyze the drifts of patterns of New York CitiBike trips under six weather factors from various aspects. The results suggest that the bike trips' responses to some weather factors fluctuate dynamically while others maintain at a relatively stable level. It is concluded that a few main influencing factors are adequate to represent the travel patterns. It is observed that dynamic models, with the strength of alleviating multicollinearity, present better forecast performance than classic models. This work can facilitate the decision makers and managers to oversee and optimise travel experience of users in real time.

The advent of Connected and Autonomous Vehicles (CAVs) and Mobile Internet technologies is reshaping the public transport sector. Autonomous buses are equipped with varying advanced sensors, and hold great promises to enhancing the responsiveness and flexibility of public transit system. In the context of CAVs, transit operators can not only optimize service headway but also adjust bus capacity to meet the time-varying passenger demand. Anticipated benefits of introducing autonomous buses to the existing transit systems include safety improvement, driver cost reduction, and optimal routing. Bus electrification is another global trend to replace traditional diesel buses for energy savings. Electric buses are advantageous to both operators and passengers due to low greenhouse gas emission, maintenance cost, as well as noise pollution. In recent years, demand responsive public transport (DRPT) services (e.g. customized bus, microtransit) receive huge success thanks to the development of Mobile Internet. Unlike traditional bus services or the fixed-route transit service that relies on passive recipients, fixed stops, and schedules, DRPT can provide personalized service for specific clients through interactive information platform (Internet or smartphone). The aforementioned new types of public transit services significantly improve service quality, reduce energy consumption, and ultimately attract more ridership. To fully explore the benefits of personalized, electric and autonomous transit systems, new analytical models and data-driven methods for transit planning and operation are needed. The authors have selected 15 articles for review in this special issue. A summary of these articles is outlined below.

Numerous fast heuristic algorithms, including shooting heuristics (SH), have been developed for real-time trajectory optimization, although their optimality has not yet been quantified. This paper compares the performance between fast heuristics and exact optimization models. We investigate a core trajectory optimization problem as a building block for numerous trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a one-lane highway when the arrival and departure times and velocity are given. To apply the SH algorithm to this problem, we adapt it to a fast-simplified shooting heuristic (FSSH) model to solve the trajectory smoothing problems with different arrival and departure velocities. An exact trajectory optimization (ETO) model is formulated that takes the vehicle position and velocity as the decision variables, and the fuel consumption and driving comfort as the objective function. The constraints of the model are based on the limits and safety of the vehicle dynamics between consecutive vehicles. We demonstrate the convexity of the ETO objective function, ensuring the solvability of the ETO model at the true optimum using gradient descent algorithms supplied by the MATLAB optimization toolbox. Six groups of numerical experiments using different input parameters and one experiment using real Next Generation Simulation (NGSIM) data are conducted. ETO can improve the objective values by a few to tens of percentage points. However, FSSH achieves a greater solution efficiency with an average solution time of less than 0.1 s compared to ~450 s for ETO.

Electrification of public transport is inspired by the increasing concern about greenhouse gas emissions. Studies in this realm were conducted to smooth the sustainable transport mode transition, whereas very little attention has been dedicated to modeling the effect of battery degradation process on fleet operation. To fill the research gap, a long-term electric fleet management framework is developed, with fully considering the practical battery capacity loss within charge and discharge cycling. As the battery aging rate is highly dependent on the state of battery charge, we propose to constrain state of battery charge within a predefined range and quantify its cost-effective feature through lifecycle cost analysis. We employ 6 groups of the selected ranges to valid the model and conduct a cost-benefit analysis through comparing their corresponding lifecycle costs. It shows that the battery lifespan can be extended by up to 3 years and the lifecycle cost of electric bus fleet can reduce 24.7% through keeping the state of battery charge within a low and narrow range. A number of managerial insights stemmed from the numerical cases were fully analyzed. The framework and results of this study were expected to serve as a reference for transit operators to make sustainable management strategy for the next generation of public transport.

The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.

Although geo-tagged mobility data (e.g., cell phone data and social media data) can be potentially used to estimate individual space-time travel trajectories, they often have low sample rates that only tell travelers' whereabouts at the sparse sample times while leaving the remaining activities to be estimated with interpolation. This study proposes a set of time geography-based measures to quantify the accuracy of the trajectory estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the possible absolute and relative error ranges between the estimated and the ground truth trajectories that cannot be observed. These measures can be used to evaluate the suitability of the estimated individual trajectories from sparsely sampled geo-tagged mobility data for travel mobility analysis. We suggest cutoff values of these measures to separate useful data with low estimation errors and noisy data with high estimation errors. We conduct theoretical analysis to show that these error measures decrease with sample rates and people's activity ranges. We also propose a lookup table-based interpolation method to expedite the computational time. The proposed measures have been applied to 2013 geo-tagged tweet data in New York City and 2014 cell-phone data in Shenzhen, China. The results illustrate that the proposed measures can provide estimation error ranges for exceptionally large datasets in much shorter times than the benchmark method without using lookup tables. These results also reveal managerial results into the quality of these data for human mobility studies, including their distribution patterns.

Transport researchers and practitioners have long been seeking capable solutions to deal with the traffic oscillations caused by freeway merging. Although existing approaches based on ramp metering have improved the overall efficiency of on-ramps, their performance is still far below the theoretical capacity. The recently proposed detecting technology of autonomous vehicles (AVs) provides an alternative for maximizing the merging efficiency by developing and using appropriate controllers for AVs. In this paper, we develop a cooperative intelligent driver model in order to examine the system performance under different proportions of AVs. The results show that, with a proper vehicle-to-vehicle controlling mechanism, an increasing percentage of AVs will reduce the total travel time and smooth traffic oscillations.

Road tunnels are critical transportation infrastructures that provide underground passageways for motorists and commuters. Fire in road tunnels in combination with tunnel safety provisions failure may lead to catastrophic consequences, and thus, necessitates a robust and reliable approach to assess tunnel risks. This article proposes a quantitative risk assessment model for fire in road tunnel by taking into consideration two types of uncertainties. A Monte Carlo-based estimation method is developed to propagate parameter uncertainty in quantitative risk assessment model consisting of event tree analysis as well as consequence estimation models. The percentile-based individual risks and$\alpha $-cut-based societal risks are put up and the risk indices are proven to be very useful for tunnel operators with distinct risk attitudes to assess the safety level of a road tunnel. Finally, the proposed research methodology is applied to Singapore KPE road tunnels.