This study investigates the problem of joint optimization of transit network design, timetable, and passenger assignment with exact transfer behavior modeling. The problem is formulated as a bi-level mixed-integer bilinear program to capture passengers’ realistic path choice behavior. The upper-level model aims to minimize the weighted sum of the cost of bus route construction, bus route operation, bus station construction, travel time of passengers, the delay caused by failures in aboarding to the bus trips at the origin, the delay caused by failures in transfer between the bus trips, and the overflow delay when the bus trip operates at capacity. The lower-level model aims to minimize the travel time of passengers. The travel time of passengers is formulated as the sum of the waiting time for boarding, the transfer time, and the in-vehicle travel time. The passenger transfer time and the delay caused by failures in transfer between the bus trips are formulated with exact modeling of passenger transfer behavior. This bi-level mixed-integer bilinear program is transformed into an equivalent mixed-integer bilinear program with equilibrium constraints using Karush-Kuhn-Tucker conditions. To seek a solution of good quality to the proposed model while not requiring a large amount of computer memory, a Benders decomposition algorithm integrated with piecewise linearization is developed. A numerical application demonstrates that the proposed model is able to achieve 3.49% lower total cost than the baseline model assuming passenger transfer time to be half of the headway.

This study investigates the problem of road side unit (RSU) location optimization considering vehicle-to-RSU (V2R) communication channel competition. To hedge against the uncertainty of vehicle density, the problem is formulated as a stochastic mixed-integer nonlinear program with equilibrium constraints. This program aims to minimize the expectation of weighted sum of V2R communication delay, packet loss rate and packet collision rate and age of information in V2R communication over all scenarios given RSU location budget limit. Decision variables are RSU locations and the number of connected autonomous vehicles (CAVs) communicating with each located RSU. Equilibrium constraints in the program model V2R communication channel competition among CAVs and ensures the choice of CAVs on RSUs to satisfy user equilibrium principle. The V2R communication is calculated under 6G technology. The program is linearized by using piecewise linearization method. To enhance the solution efficiency, a progressive hedging algorithm is developed to decompose the relaxed linearized model into several subproblems. The optimal solution to the relaxed linearized model is found by iteratively formulating and the solving subproblems. A branch and bound algorithm is introduced to obtain the optimal integer solution to the linearized model. The numerical results show that the proposed model can achieve 20.55% lower total communication delay than the state-of-the-art model only optimizing total V2R information propagation delay, when CAVs choose RSUs for communication in a competitive manner.

This paper proposes a methodology combining Long-Short-Term-Memory (LSTM)-assisted kinematic motion prediction with a hybrid coding algorithm for compressing the trajectory data of Connected Autonomous Vehicles (CAVs). The vehicle locations after the first two time steps are predicted based on the vehicle positions at the first two time steps and the kinematic equation. The vehicle velocities and accelerations are predicted based on the vehicle locations and LSTM. The hybrid coding algorithm integrates differential coding, Binary Coded Decimal (BCD) coding and arithmetic coding. Differential coding converts the original data into the difference between the original data and the predicted data. Since the length of the original data is large but the difference between it and predicted data is small, the required space for storing the data can be greatly reduced. BCD coding converts subsequences of different lengths to the subsequences with the same length so that the original information can be correctly reproduced after decompression. Arithmetic coding expresses the information in small space by converting the character sequence into a decimal between 0 and 1. The proposed algorithm is evaluated on the Next Generation Simulation Trajectory dataset. The experiment results show that the compression ratio and compression rate obtained by the proposed algorithm are respectively higher and lower than those obtained by the baseline algorithms. Also, the sum of compression time, decompression time and transmission time associated with the proposed algorithm is less than that associated with most baseline algorithms and transmission without compression.

This study deals with the problem of road side unit (RSU) location optimization for origin-destination (OD) demand estimation. With the point-to-point measurement provided by RSUs in connected vehicle environment, the errors of OD demand estimation come from two sources: 1) the lack of enough path flow information; and 2) the vehicle-to-RSU (V2R) communication delay. However, increasing the amount of path flow information collected by RSUs results in the increase of V2R communication delay encountered by each collected data packet. Moreover, it is difficult to find a global optimal solution by formulating the problem as a single objective program. To address the investigated problem, this study proposes a novel framework consisting of solving a bi-objective RSU location optimization problem and an OD demand estimation problem. This RSU location optimization problem is formulated as a bi-objective nonlinear binary integer program to balance the maximization of the amount of path flow information and the minimization of V2R communication delay. The OD demand estimation problem is formulated as a least square estimator to identify the RSU location scheme with the smallest OD demand estimation error, among the Pareto optimal solutions to the bi-objective program. An efficient$\varepsilon $-constraint method is developed to generate the Pareto optimal solutions. The numerical example demonstrates that the proposed framework achieves 6.95 lower root-mean-square error of OD demand estimation, compared with the baseline framework.

This study investigates the problem of roadside unit (RSU) location optimization for information propagation under stochastic traffic conditions. The goal of RSU location optimization is to promote multihop information propagation in the Internet of Vehicles which is the promising application of the Internet of Things in transportation. Considering the information propagation time is significantly affected by traffic density and traffic density is endowed with randomness, the problem is formulated as a two-stage mixed-integer nonlinear stochastic programming. The model aims to minimize the sum of the cost associated with RSU investment and the expectation of the penalty cost associated with the network information propagation time exceeding an acceptable threshold. In the first stage of the programming, the number and location of RSUs are determined when network-wide traffic density is not realized. In the second stage, given the RSU location schemes determined in the first stage and the realization of traffic density, the information propagation shortest paths are determined for all origin-destination pairs to minimize network information propagation time. A genetic algorithm (GA) integrated with the solution of a mixed-integer linear programming (GA-MILP) is proposed to solve the model. Numerical results indicate that the advantage of the proposed model in the reduced information propagation time per cost over the deterministic model can be up to 15.54%. Compared with the conventional GA, the GA-MILP has 10.01% higher computation efficiency. This further leads to a 14.73% lower objective value achieved by the GA-MILP when the number of iterations is 50.