Automated driving has recently attracted significant attention. While considerable research has been conducted on the technologies and societal acceptance of autonomous vehicles, investigations into the control and scheduling of urban automated driving traffic are still nascent. As automated driving gains traction, urban traffic control logic is poised for substantial transformation. Presently, both manual and automated driving predominantly operate under a local decision-making traffic mode, where driving decisions are based on the vehicle’s status and immediate environment. This mode, however, does not fully exploit the potential benefits of automated driving, particularly in optimizing road network resources and traffic efficiency. In response to the increasing adoption of automated driving, it is essential for traffic bureaus to initiate proactive dialogs regarding urban traffic control from a global perspective. This paper introduces a novel global control mode for urban automated driving traffic. Its core concept involves the central scheduling of all autonomous vehicles within the road network through vehicle-infrastructure cooperation, thereby optimizing traffic flow. This paper elucidates the mechanism and process of the global control mode. Given the operational complexity of expansive road networks, the paper suggests segmenting these networks into multiple manageable regions. This mode is conceptualized as an autonomous vehicle global scheduling problem, for which a mathematical model is formulated and a modified A-star algorithm is developed. The experimental findings reveal that (i) the algorithm consistently delivers high-quality solutions promptly and (ii) the global scheduling mode significantly reduces traffic congestion and equitably distributes resources. In conclusion, this paper presents a viable and efficacious new control mode that could substantially enhance urban automated traffic efficiency.
In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.
This study investigates the use of autonomous vehicles in bus rapid transit lanes during the initial phases of autonomous driving development. The aim is to accelerate the advancement of autonomous driving technologies and enhance the efficiency of bus lane usage. We first develop a dynamic joint optimization model that adjusts autonomous vehicle speeds and bus timetables to minimize vehicle travel times while reducing bus passenger waiting times. We account for random variables such as stochastic passenger arrivals at bus stations and variable demand for autonomous vehicle travel by constructing a stochastic dynamic model. To address the computational challenges of large-scale scenarios, we implement a simulation-based heuristic algorithm framework. This framework is designed to efficiently produce high-quality solutions within feasible time limits. Our numerical studies on an actual bus line show that our approach significantly improves system throughput compared to existing benchmarks. Moreover, by strategically managing the entry of autonomous vehicles into the lane and modifying bus timetables, we further enhance the operational efficiency of the system.
Urban rail transit (URT) plays a pivotal role in mitigating urban congestion and emissions, positioning it as a sustainable transportation alternative. Nevertheless, URT’s function in transporting substantial numbers of passengers within confined public spaces renders it vulnerable to the proliferation of infectious diseases during public health crises. This study proposes a decision support model that integrates operational control strategies pertaining to passenger flow and train capacity utilization, with an emphasis on energy efficiency within URT networks during such crises. The model anticipates a URT system where passengers adhere to prescribed routes, adhering to enhanced path flow regulations. Simultaneously, train capacity utilization is intentionally limited to support social distancing measures. The model’s efficacy was assessed using data from the COVID-19 outbreak in Xi’an, China, at the end of 2021. Findings indicate that focused management of passenger flows and specific risk areas is superior in promoting energy efficiency and enhancing passenger convenience, compared to broader management approaches.
Bus bunching has been a persistent issue in urban bus system since it first appeared, and it remains a challenge not fully resolved. This phenomenon may reduce the operational efficiency of the urban bus system, which is detrimental to the operation of fast-paced public transport in cities. Fortunately, extensive research has been undertaken in the long development and optimization of the urban bus system, and many solutions have emerged so far. The purpose of this paper is to summarize the existing solutions and serve as a guide for subsequent research in this area. Upon careful examination of current findings, it is found that, based on the different optimization objects, existing solutions to the bus bunching problem can be divided into five directions, i.e., operational strategy improvement, traffic control improvement, driver driving rules improvement, passenger habit improvement, and others. While numerous solutions to bus bunching are available, there remains a gap in research exploring the integrated application of methods from diverse directions. Furthermore, with the development of autonomous driving, it is expected that the use of modular autonomous vehicles could be the most potential solution to the issue of bus bunching in the future.
The widespread use of energy storage systems in electric bus transit centers presents new opportunities and challenges for bus charging and transit center energy management. A unified optimization model is proposed to jointly optimize the bus charging plan and energy storage system power profile. The model optimizes overall costs by considering battery aging, time-of-use tariffs, and capacity service charges. The model is linearized by a series of relaxations of the nonlinear constraints. This means that we can obtain the exact solution of the model quickly with a commercial solver that is fully adapted to the time scale of day-ahead scheduling. The numerical simulations demonstrate that the proposed method can optimize the bus charging time, charging power, and power profile of energy storage systems in seconds. Monte Carlo simulations reveal that the proposed method significantly reduces the cost and has sufficient robustness to uncertain fluctuations in photovoltaics and office loads.
The expansion of e-commerce and the sharing economy has paved the way for crowdshipping as an innovative approach to addressing last-mile delivery challenges. Previous studies and implementations have predominantly concentrated on private vehicle-based crowdshipping, which may lead to increased traffic congestion and emissions due to additional trips made specifically for deliveries. To circumvent these possible adverse effects, this paper explores a public transport (PT)-based crowdshipping concept as a complementary solution to the traditional parcel delivery systems. In this model, PT users leverage their routine journeys to perform delivery tasks. We propose a methodology that includes a parcel locker location model and a vehicle routing model to analyze the effect of PT-based crowdshipping. Notably, the parcel locker location model aids in planning a PT-based crowdshipping network and identifying obstacles to its development. A case study conducted in the central district of Copenhagen utilizing real-world data assesses the effects of PT-based crowdshipping. The findings suggest that PT-based crowdshipping can decrease the total kilometers traveled by vehicles, the overall working hours of drivers, and the number of vans required for last-mile deliveries, thereby alleviating urban traffic congestion and environmental pollution. Nevertheless, the growth of PT-based crowdshipping may be limited by the availability of crowdshippers, indicating that initiatives to increase the number of crowdshippers are essential.
This paper investigates whether e-hailing performs better than on-street searching for taxi services. By adopting the Poission point process to model the temporal-spatial distributions of idle vehicles, passengers’ waiting time distributions of on-street searching and e-hailing are explicitly modeled, and closed-form results of their expected waiting time are given. It is proved that whether e-hailing performs better than on-street searching mainly depends on the density of idle vehicles within the matching area and the matching period. It is proved that given the advantage of e-hailing in rapidly pairing passengers and idle vehicles, the expected waiting time for on-street searching is always longer than that of e-hailing as long as the number of idle vehicles within a passenger’s dominant temporal-spatial area is lower than 4/π. Moreover, we extend our analysis to explore the market equilibria for both e-hailing and on-street searching, and present the equilibrium conditions for a taxi market operating under e-hailing versus on-street searching. With a special reciprocal passenger demand function, it is shown that the performance difference between e-hailing and on-street searching is mainly determined by the ratio of fleet size to maximum potential passenger demand. It suggests that e-hailing can achieve a higher capacity utilization rate of vehicles than on-street searching when vehicle density is relatively low. Furthermore, it is shown that an extended average trip duration improves the chance that e-hailing performs better than on-street searching. The optimal vehicle fleet sizes to maximize the total social welfare/profit are then analyzed, and the corresponding maximization problems are formulated.
This paper proposes an algorithm for train delay propagation on double-track railway lines under First-Come-First-Serve (FCFS) management. The objective is to handle the challenges faced by the dispatchers as they encounter train delays and their effects on the functioning of the railway system. We assume that the location and duration of disruptions are known, which are important inputs to the algorithm. This data enables calculation of delays experienced by each affected train. Our method analyzes factors such as train schedules, track capacities, and operation constraints to assess the manner in which delays would get propagated along railway lines. Key indicators of delay propagation, consisting of the number of delayed trains and stations, disruption settling time, and cumulative delays, are considered. Moreover, a numerical example is given to explain the practical application of this algorithm. Finally, we show that a tool like this would facilitate the dispatchers in managing and rescheduling trains in case of delays and will be improving resilience and efficiency of railway operations.
This study explores urban air mobility (UAM) as a strategy for mitigating escalating traffic congestion in major urban areas as a consequence of a static transportation supply versus dynamic demand growth. It offers an in-depth overview of UAM development, highlighting its present state and the challenges of integration with established urban transport systems. Key areas of focus include the technological advancements and obstacles in electric vertical take-off and landing (eVTOL) aircrafts, which are essential for UAM operation in urban environments. Furthermore, it explores the infrastructure requirements for UAM, including vertiport deployment and the creation of adept air traffic control (ATC) systems. These developments must be integrated into the urban landscape without exacerbating land-use challenges. This paper also examines the regulatory framework for UAM, including existing aviation regulations and the necessity for novel policies specifically designed for urban aerial transport. This study presents a comprehensive perspective for various stakeholders, from policymakers to urban planners, highlighting the need for a thorough understanding of UAM’s potential and effective assimilation into urban mobility frameworks.
This paper investigates the impact of ride-hailing services, particularly the integration of autonomous vehicles (AVs), on urban transportation systems. The paper discusses the challenges faced by ride-hailing platforms in managing a fleet of both AVs and conventional vehicles (CVs) within the spatial network of a city. It examines the approaches and methods used to manage demand allocation for AVs and CVs, considering the strategic behavior of human drivers and considerations for possible regulations. Using mean-field game theory, this paper proposes efficient strategies for managing fleet operations along with those of traffic optimization and service efficiency. The analysis highlights the complexities of integrating AVs into existing transportation systems and advocates for the development of robust theoretical traffic models for regulatory decisions and improved urban mobility.
Mega infrastructure projects (MIPs) play crucial roles in promoting social development, regional growth, and disaster and crisis resilience. These complex projects frequently face challenges in stakeholder management, which might be a risk for their sustainability. Hence, this paper proposes affordance theory as a new theoretical framework, particularly on the basis of understanding and managing MIPs. This paper aims to achieve three main objectives: 1) conceptualizing MIP affordance, 2) documenting the influence of MIP affordance on stakeholder management and project sustainability, and 3) developing strategies for managing MIP affordance. It applies critical realism to conceptualize MIP affordance and its mechanism, and employs the expectation-confirmation theory to identify critical determinants for managing MIP affordance. The paper thus provides new knowledge and insights into the management of MIP stakeholders concerning project sustainability.
