Journal of Transportation Engineering, Part A: Systems

Two principal aspects of preliminary highway design are detailed characterization of the geographic and topographic features along potential candidate alignments and preliminary design of the highway along at least one of these candidates. The digital elevation model (DEM) of the existing terrain is essential for these tasks. However, generating a DEM requires excessive time and effort. This research paper proposes a quick and practical technique to extract geographic and topographic data from freely available online resources to generate a DEM for purposes of preliminary highway geometric design. A primary step in construction of the DEM was to quantify relationships between actual distance on the ground, computer screen resolution and size, and scale of the virtual earth, and then use these relationships to extract the data automatically. The area displaying position and elevation data were positioned and subsequently saved as images. Image recognition techniques were then used to interpret the position and elevation data. Data storage management was subsequently conducted to minimize the error generated due to decimal place omission. In doing so, the accuracy of the extracted geographic data were enhanced to meet the requirements for preliminary geometric design. The DEMs were then constructed using the extracted geographic data. To demonstrate the efficacy of the developed method and to verify its accuracy, the paper used data from a mountainous region located in the Sichuan Province of China. The results showed that the proposed method is practical and that the constructed DEM is reliable and reasonable. Further, compared with traditional methods, the proposed DEM construction method is less time-consuming and laborious, and more functionally consistent with available online resources.

High-occupancy toll (HOT) lanes can better utilize road resources by allowing low-occupancy vehicle (LOV) drivers to pay a toll and use high-occupancy vehicle (HOV) lanes. In such a system, the toll price plays a key role in dynamically allocating LOVs over the HOT and general purpose (GP) lanes to improve the overall system performance. This paper presents a method to model the lane-choice behavior and dynamically determine the toll price in response to traffic conditions. First, a combined model of $k$-fold cross validation ($k\text{-}\mathrm{CV}$), particle swarm optimization (PSO), and support vector regression (SVR) is proposed to predict the lane-choice behavior of LOVs, which is an important factor to determine the optimal toll. Five-minute tolling data collected from Interstate 405 have been used for the study. Compared with five different methods, this combined model showed the highest accuracy of 92.37%. Based on the model, the relationship between the toll price, the real-time traffic speed and volume in GP and HOT lanes, and the number of lane-changing LOVs can be identified. Next, a toll pricing optimization model was developed in order to minimize the total travel time as well as determine the corresponding optimal volume of lane-changing LOVs. Based on the previously identified relationship, an optimal toll can be calculated dynamically according to the optimal number of lane-changing LOVs and the real-time speed and volume. The study further analyzes the benefit of the proposed price optimization method in terms of travel time saving. The results show that the dynamic tolling strategy helps to better utilize the capacity of HOT lanes as well as bring significant reduction in the total travel time.

The article presents a method for ranking various catchment areas of an urban area according to their current service condition of the walk environment and their improvement needs. The study proposes a rationally appealing methodological framework for preparing a ranking order of catchment areas, considering road users’ perceived responses on several individual survey items describing the walk environment of a particular catchment area instead of the overall perception of the walk environment. The rank orders are prepared using three ranking methods: relative to an identified distribution integral transformation (RIDIT), technique for order preference by similarity to ideal solution (TOPSIS), and grey relational analysis (GRA). The outcomes of rank order from these ranking methods were compared pairwise to check whether any monotonicity existed among the outcomes, and then to decide on selection of the final rank order. The method was demonstrated by considering geographical area of the Bhubaneswar city, India, and the study presented several new findings to transportation planners and practitioners with reference to developing countries.

Light detection and ranging (LiDAR) data provide a rather precise depiction of the real three-dimensional (3D) road environment and have been used by some researchers to produce more precise available sight distance (ASD) results compared with those obtained based on conventional digital elevation models with low resolution. However, existing methods have some difficulties in creating digital surface models to accurately estimate ASD using LiDAR data. In addition, dynamic visualization of the driver’s visual conditions along the highway throughout ASD assessment (which is important for monitoring the results in real time) has not been achieved by existing studies. To fill these gaps, this paper discusses the development of a new procedure supported by MATLAB for evaluating, in a real-time visualization manner, ASD along an existing highway based on LiDAR data. With an innovative algorithm that combines cylindrical perspective projection and modified Delaunay triangulation, the computation is processed in real time along the vehicle trajectory, which is represented by a set of points, whereas the driver’s successive perspective views and sight distance results are generated simultaneously. A comparative case study is presented to demonstrate that the new method is more accurate than conventional methods and more flexible for evaluating ASD along highways with complicated roadside components.

This study presents an optimal variable speed limit (VSL) strategy in a connected autonomous vehicle (CAV) environment for a freeway corridor with multiple bottlenecks. The VSL control was developed by using an extended cell transmission model (CTM) which takes into account capacity decrease and mixed traffic flow, including traditional human-driven cars and heavy vehicles, and autonomous vehicles (AVs). A multiple-objective function was formulated which aims to improve the operational efficiency and smooth the speed transition. A genetic algorithm (GA) was adopted to solve the integrated VSL control problem. A real-world freeway stretch was selected to test the designed control framework. Sensitivity analyses were performed to investigate impacts of both the penetration rate of CAVs and communication range. Simulation performances demonstrated that the developed VSL control not only improves the overall efficiency but also reduces tailpipe emission rate. Simulation results also showed that the VSL control integrating vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication outperforms the VSL control only. In addition, as the penetration rate of CAVs increases, better performance can be achieved.

This paper is intended to mine historical data by presenting a scenario clustering approach to identify appropriate scenarios for mesoscopic simulation as a part of the evaluation of transportation projects or operational measures. It provides a systematic and efficient approach to select and prepare effective input scenarios for a given traffic simulation model. The scenario clustering procedure has two primary applications: travel time reliability analysis, and traffic estimation and prediction systems. The ability to systematically identify similarity and dissimilarity among weather scenarios can facilitate the selection of critical scenarios for reliability studies. It can also support real-time weather-responsive traffic management (WRTM) by quickly classifying a current or predicted weather condition into predefined categories and suggesting relevant WRTM strategies that can be tested via real-time traffic simulation before deployment. A detailed method for clustering weather time series data is presented and demonstrated using historical data. Two clustering algorithms with different similarity measures are compared. Clustering results using a $k$-means clustering algorithm with squared Euclidean distance are illustrated in the travel time reliability application.

Bus speed indicates the performance of bus systems. Exploring the impact of contributing factors to bus speed can provide public transit agencies insights into developing improvement strategies. Bus speed observations can be correlated both spatially and temporally, but their dependence has generally been neglected. This paper proposes a novel multivariate conditional autoregressive (MCAR) model to jointly account for spatial and temporal correlations of bus speeds extracted from large-scale Global Positioning System data. The proposed MCAR model is compared with the univariate conditional autoregressive model, which only accounts for spatial correlation, and the linear regression model, which assumes independent speed observations. Results show that the MCAR model outperforms the other models by presenting a much lower deviance information criterion (DIC) value and smaller prediction errors. This confirms the necessity of addressing the spatiotemporal correlation when modeling bus speeds. Driveway density, number of bus routes, bus stop density, signal effect, and bus volume are found to affect bus speed significantly in the MCAR model. Furthermore, how the distance affects the spatial correlation is investigated by specifying different spatial correlation weight (SCW) matrices. It is found that the MCAR model with SCWs generated from the radial basis function (RBF) can yield better outcomes than one using inverse distance. The optimal shape parameter of the RBF is found to be within a range of 1–2 km. Specifically, if the shape parameter equals 2 km, the SCW of two road segments is approximately 0.88 when their midpoints are 1 km from each other.

This study proposes a quantitative approach to evaluate the effects of mixed traffic flow on bus running times (except bus dwell times) near bus-stop areas based on linear regression and survival analysis theory. Research data were collected by video cameras at four bus stops in Nanjing, China. The application of the proposed methods with field data indicated that several factors would delay bus running times, i.e., car and nonmotor volume, bus dwell time, bus berth without violation, and nonmotor violations. In addition, the effect of bus lane-changing behavior on bus running times varied from section to section. Moreover, linear and parametric survival models were also developed to estimate bus running times, and both models can capture the effect of factors. The parametric survival models have better fitness performance than the linear models. However, the predictive performance of the two models are distinct at different sections and bus stops. The findings of influential factors and the proposed models could be considered in advanced bus information systems.

A simultaneous bus route network design and frequency setting model is formulated as the weighted sum multiobjective genetic algorithm optimization. It aims to search for a near-optimum set of routes and corresponding frequencies while minimizing both user and operator costs. It is a nondeterministic polynomial time complete optimization problem, and searching the entire space through metaheuristic algorithms is impractical. It does require narrowing down the search space into some feasible regions. In this paper, two strategies are implemented, offering a distinct advantage in the finding of near-optimum solutions. First, a set of constraints are imposed on resources defined through experience and route generation and filtration algorithms are implemented to increase the quality of the candidate solution pool. Second, in order to increase the probability of selecting better performing set of routes and reducing genetic algorithm’s degree of randomness, a specially designed transfer minimization operator is implemented within the optimization procedure. Results are then compared on the basis of Mandl’s network that has been used as the benchmark by various researchers. Further, the sensitivity of the model to different rules and parameters is carried out. Obtained solutions perform better as compared to the previous techniques.