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The goods collection and delivery process often relates to distribution logistics problems. The task is to deliver goods from warehouses to customers under specific circumstances. Efforts to optimize the process are largely aimed at reducing overall costs of goods transportation. Among the prominent algorithms for solving the basic type of the delivery (or collection) problem, which includes a single depot and a homogeneous vehicle fleet, is the algorithm developed by Clarke and Wright in 1964. This algorithm minimizes transportation costs by maximizing the savings achieved through merging multiple routes into one. This paper primarily aims to solve the pickup and delivery problem where the goods must be delivered and empty packaging collected in a single process. The request of a customer can be routed from the depot or from another customer. Similarly, the destination of the request may be the depot or another customer. Unlike the original version of the Clarke and Wright algorithm, the initial routes are created to satisfy delivery orders, and therefore, the same customer can occur in multiple routes. Consequently, a situation may arise in which two routes containing one or more common vertices must be combined during the calculation. Furthermore, these vertices need not be the outermost vertices of the routes. This situation cannot be addressed by using the original version of the Clarke and Wright algorithm, and that is why we propose its modification. Merging routes through inner vertices means that the cost savings depend on the configurations of the routes, and therefore, they cannot be calculated a priori. Instead, the dynamic savings matrix must be used. [ABSTRACT FROM AUTHOR]

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane-changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane-changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane-changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two-lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane-changing location matching scheme can save 5.9% total travel time when compared to the near-end location lane-changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane-changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane-changing vehicles increase, our method could perform better., Author(s): Gao Gao [1,2]; Zhengfeng Huang (corresponding author) [1,2]; Wei Ji [1,2]; Pengjun Zheng [1,2] 1. Introduction To realize the coordinated optimization of vehicles, roads, and networks to alleviate related [...]

In recent years, severe traffic congestion in urban areas has become a growing concern for many cities in China. While beltway expressways have been built to alleviate urban congestion by diverting traffic away from city centers, existing preferential access schemes have led to overuse of certain beltway links and a decline in service levels. This research is motivated by the need to develop an improved scheme that balances urban traffic relief and beltway service level maintenance. To benefit both the government and the beltway expressway company, we propose a tradable credit scheme (TCS) to shift some of the traffic burden from urban roads to the beltway expressway while maintaining its current service level. In order to determine an appropriate TCS, a biobjective bilevel programming model is introduced. The upper-level model seeks to maximize the revenue of the beltway expressway company while minimizing the system travel time as specified by the traffic management department. The lower-level model describes user travel behavior under given TCS and capacity constraints of beltway expressway links, represented as the generalized capacitated traffic assignment problem. Additionally, an NSGA-II framework is designed to discover Pareto-optimal TCSs. Within this framework, a gradient projection method is utilized within the augmented Lagrangian multiplier algorithm to solve the lower-level problem and evaluate the fitness of the TCS. Numerical experiments demonstrate that implementing well-designed tradable credit schemes can effectively alleviate urban traffic congestion, reducing system travel time by up to 1.6%, while simultaneously increasing the beltway company's revenue by up to 300% compared to the base case without requiring government subsidies. The sensitivity analysis results indicate the insufficiency of the existing preferential access or toll-free schemes on the beltway, highlighting the greater effectiveness of the integrated strategy proposed in this study., Author(s): Jimeng Tang [1]; Yongxiang Zhu [1]; Yue Yu (corresponding author) [1]; Lu Cai [1] 1. Introduction Traffic congestion poses a significant challenge with adverse impacts on the environment, public [...]

In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation., Author(s): Jaime Villa (corresponding author) [1]; Franz García [1]; Rubén Jover [2]; Ventura Martínez [2]; José M. Armingol [1] 1. Introduction In the 21st century, where mobility is at the [...]