This article is the first in the literature to investigate the network traffic equilibrium for traveling and parking with autonomous vehicles (AVs) under a fully automated traffic environment. Given that AVs can drop off the travelers at their destinations and then drive to the parking spaces by themselves, we introduce the joint equilibrium of AV route choice and parking location choice, and develop a variational inequality (VI)‐based formulation for the proposed equilibrium. We prove the equivalence between the proposed VI model and the defined equilibrium conditions. We also show that the link flow solution at equilibrium is unique, even though both the route choices and parking choices are endogenous when human‐occupied AV trips (from origin to destination) and empty AV trips (from destination to parking) are interacting with each other on the same network. We then develop a solution methodology based on the parking‐route choice structure, where we adjust parking choices in the upper level and route choices in the lower level. Numerical analysis is conducted to explore insights from the introduced modeling framework for AV network equilibrium. The results reveal the significant difference in network equilibrium flows between the AV and non‐AV situations. The results also indicate the sensitivity of the AV traffic pattern to different factors, such as value of time, parking pricing, and supply. The proposed approach provides a critical modeling device for studying the traffic equilibrium under AV behavior patterns, which can be used for the assessment of parking policies and infrastructure development in the future era of AVs.

This study is, to our knowledge, the first in the literature to introduce a modeling framework for analyzing traffic crash frequency based on a series of ensemble machine learning (EML) methods. The main objectives of this study are fourfold: (a) to design a systematic EML-based framework for crash frequency analysis, (b) to comprehensively compare the performance in analyzing crash frequency by different optimized EML models, (c) to identify significant contributors to crash frequency, and (d) to propose the approach to construct schemes to reduce traffic crashes. To achieve the research goal, the Highway Safety Information System database that includes records of over 1.5 million crashes is employed for model estimation and validation. We first optimize the EML models for crash analysis via the k-fold cross-training, including the two averaging methods of random forest and extremely randomized trees, and the two boosting methods of adaptive boosting and gradient tree boosting. Then, we assess the behavior of the optimized models, and conduct a sensitivity test to validate the stability of model performance. Furthermore, we evaluate the relative importance of features to crash frequency by using the Gini diversity index. The results indicate that the two averaging EML models can achieve desirable performance in crash frequency analysis, which outperform the two boosting EML models, in terms of predictive accuracy, generalization ability, and stability. From the results, we explore new insights into the significance of contributors to crash occurrence. Finally, we present the approach of safety improvements for transport facilities.

The motivation of this study is to minimize the system‐level travel time costs and greenhouse emissions, which include tailpipe emissions by internal combustion engine vehicles (ICEVs) and smokestack emissions indirectly caused by electric vehicles (EVs), while satisfying EVs’ replenishment need in transport networks subject to financial restraints for infrastructure development. In this study, we address recharge facility locations of EVs, where two types of recharge services are taken into account, that is, traditional charging stations and modern charging lanes. The multitype recharge facility location problem is formulated by employing the bilevel framework of the network design problem. In the lower‐level program, the mixed‐vehicular traffic assignment problem with en‐route multitype recharge is employed, which accounts for both ICEVs and EVs with various driving ranges. The upper‐level program aims to minimize the total system travel costs by selecting the optimal solution from a set of infrastructure design options considering both expansions of road capacities and provisions of multitype recharge facilities for EVs. In the algorithmic framework, we propose a tailored metaheuristic to solve medium to large instances. Systematic evaluation is conducted to test the efficacy of the proposed approach. The results highlight the impacts of traffic composition, distance ranges of EVs, budget levels and facility expenses on the project selection and evaluation. The results indicate that the two design objectives, to respectively minimize the network‐wide travel time and greenhouse emissions, are conflicting for certain scenarios. Additionally, the results demonstrate the advantages of the network design problem (NDP) considering both multitype recharge service provision and road capacity enhancement over the NDP accounting for the former only.

A traffic assignment model is a critical tool for developing future transport systems, road policies, and evaluating future network upgrades. However, the development of the network and demand data is often highly intensive, which limits the number of cases where some form of the models are available on a global basis. These problems include licensing restrictions, bureaucracy, privacy, data availability, data quality, costs, transparency, and transferability. This paper introduces Rapidex, a novel origin–destination (OD) demand estimation and visualisation tool. Firstly, Rapidex enables the user to download and visualise road networks for any city using a capacity-based modification of OpenStreetMap. Secondly, the tool creates traffic analysis zones and centroids, as per the user-specified inputs. Next, it enables the fetching of travel time data from pervasive traffic data providers, such as TomTom and Google. With Rapidex, we tailor the genetic-algorithm (GA)-based metaheuristic approach to derive the OD demand pattern. The tool produces critical outputs such as link volumes, link travel times, OD travel times, average trip length and duration, and congestion level, which can also be used for validation. Finally, Rapidex enables the user to perform scenario evaluation, where changes to the network and/or demand data can be made and the subsequent impacts on performance metrics can be identified. In this article, we demonstrate the applicability of Rapidex on the network of Sydney, which has 15,646 directional links, 8708 nodes, and 178 zones. Further, the model was validated using the Household Travel Survey data of Sydney using the aggregated metrics and a novel project selection method. We observed that 88% of the time, the “estimated” and “observed” OD matrices identified the same project (i.e., the rapid process estimated the more intensive traditional approach in 88% of cases). This tool would help practitioners in rapid decision making for strategic long-term planning. Further, the tool would provide an opportunity for developing countries to better manage traffic congestion, as cities in these countries are prone to severe congestion and rapid urbanisation while often lacking the traditional models entirely.