An Algorithm Based on Mobile Agent for Delay Constrained Least Cost Unicast Routing

The delay constrained least cost path problem with imprecise delay information is discussed, and a distributed heuristic algorithm without any assumption of imprecise state information is presented. The algorithm employs mobile agents to search feasible paths in parallel and requires limited network state information kept at each node. The simulations indicate that the presented solution provides better call acceptance probability and better fairness between short paths and long paths. And the algorithm can tolerate high degree of delay imprecision.

Making Data-Driven Transportation Decisions for Freight Operations

Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.

Service Caching and Task Offloading for Mobile Edge Computing-Enabled Intelligent Connected Vehicles

The development of intelligent connected vehicles(ICVs)has tremendously inspired the emergence of a new computing paradigm called mobile edge computing(MEC),which meets the demands of delay-sensitive on-vehicle applications.Most existing studies focusing on the issue of task offloading in ICVs assume that the MEC server can directly complete computation tasks without considering the necessity of service caching.However,this is unrealistic in practice because a large number of tasks require the use of corresponding third-party libraries and databases,that is,service caching.Therefore,we investigate the delay optimization in an MEC-enabled ICVs system with multiple mobile vehicles,resource-limited base stations(BSs),and one cloud server.We aim to determine the optimal service caching and task offloading decisions to minimize the overall system delay using mixed-integer nonlinear programming.To address this problem,we first convert it into a quadratically constrained quadratic program and then propose an efficient semidefinite relaxation-based joint service caching and task offloading(JSCTO)algorithm to obtain the service caching and task offloading decisions.In the simulations,we validate the efficiency of our proposed method by setting different numbers of vehicles and the storage capacity of BSs.The results show that our proposed JSCTO algorithm can significantly decrease the total delay of all offloaded tasks compared with the cloud processing only scheme.

OPTIMAL AND INCENTIVE COMPATIBLE PRICING FOR HETEROGENEOUS PERIODS

This paper studies a service firm whose business time can be divided into several periods, each providing different value to customers. Heterogeneous service is a major reason resulting in imbalances between supply and demand. Since customers differ in their degree of impatience, firrns can use differential pricing mechanisms to optimize their objectives and match supply with demand in each period, by inducing customers to choose different periods. We study two types of firrns, an internal firm, the objective of which is to maximize the system＇s （including the f＇nan and all the customers） total net value, and a commercial firm, which aims to optimize its own profit. Though impatience factors are customers＇ private information, for each type of firm, we derive the optimal incentive compatible pricing policy, under which all the coming customers will follow the firm＇s assignment, that is, patient customers will buy the service in high-value periods, but their waiting time will be longer, while impatient customers will enter into the low-value periods, but they will be compensated by shorter waiting times. Furthermore, in the internal firm, we also prove that this mechanism enables the decentralization of decisions, while maintaining centralized system-wide optimality. Numerical analysis shows that when there is sufficient capacity, the internal firm does not always need to set lower prices than the commercial firm in every period.