Journal of Transportation Engineering, Part A: Systems

Many research works have focused on solving facility location problems to optimize the distribution of direct current fast charging (DCFC) stations along highways. However, before such optimization studies can be done, a reasonable estimate is needed of the required number of DCFCs for these highways. Unfortunately, many highways lack the detailed traffic count data required to make these estimates. This study developed a methodology for forecasting the need for DCFC stations along highways using only classic traffic count information such as annual average daily traffic (AADT), which is one of the most readily available types of data in many countries, including Canada. This method was developed using data from highway sections with more detailed traffic count information. Detailed historical traffic data of different highway sections first are analyzed thoroughly and categorized into groups of traffic flow patterns that then can be employed to predict traffic flow for other locations where only less-detailed data, such as the AADT, are available. The methodology describes a way to estimate the peak traffic flow and the long-distance traffic fraction on the highway, so that the equation developed to predict the number of long distance–traveling electric vehicles (EVs) is complete and solvable. The methodology was applied in two case studies for different highway sections in Ontario, and the need for DCFCs under various scenarios of the EV adoption rate was presented. The case studies showed that the methodology developed in this study can be used successfully to guide the planning of EV fast charging infrastructure along highways using only conventional traffic data.

In this paper, a model to optimize a high-speed railway operation plan by dividing it into a train route plan and train stop schedule plan was developed based on the train capacity and service frequency. To capture the intersection between the operator and the passenger, a multiobjective model was established for a high-speed railway corridor. This multiobjective model can be divided into two submodels. For the operator, the train empty seat minimization model seeks to improve the effective utilization of the seats, whereas for passengers, the goal is to minimize total travel time costs. The train and station service capacities and constraints were considered to better model the operation of trains on a high-speed railway line. Furthermore, to obtain a feasible solution to railway operation planning, a greedy heuristics algorithm is proposed, which included two stages. Based on the real data of the Beijing-Shanghai high-speed railway corridor in China, the effectiveness of the proposed model and method were verified.

The sectional volume of travelers refers to the number of travelers crossing a section boundary (e.g., river, mountain, railway line, etc.) within a certain time period. Mobile phone data provides continuous and large-scale mobility pattern information without compromising the comprehensiveness of travel modes. We propose a three-stage framework to estimate the sectional volume of travelers using the base station trajectory from massive mobile phone data. In the first two stages, the spatial and temporal uncertainties of trajectories are explicitly addressed by a hybrid filtering algorithm and a cell-to-cell trajectory inference algorithm, respectively. In the third stage, the sectional volume of travelers is estimated using aggregated trajectories. The proposed framework is validated using a sampled dataset with annotated ground truth and a city-scale dataset. The results show that the proposed framework is effective in dealing with spatial and temporal uncertainties of trajectories. The sectional volume estimation method performs stably with a low average error rate and is applicable to section boundaries of different scales.

In mixed traffic streams, especially near intersections, bus priority treatments are necessary to prevent buses from significant travel time losses. This paper aims to assist practitioners and field engineers to identify the design parameters and potential impacts of implementation of a queue jump lane (QJL) with presignal based on several locational characteristics. The impacts of nine different factors on the operations of QJL are evaluated using a validated traffic microsimulation model. The levels of each factor are specified based on the urban transport operations in India. In order to assist practitioners, simplistic travel time savings and maximum queue length prediction models are developed using multiparameter regression analyses based on the factors that reveal significant impacts. Finally, guidance is provided to practitioners in the form of an eight-step procedure to implement QJL in emerging countries with analogous operating conditions.

This study models and evaluates the accessibility to public transit and public transit equity by integrating the general transit feed specification (GTFS) data. A transit gap index (TGI) and a geographic information system (GIS)-based methodology are developed to conduct the assessment. The TGI is developed by taking into consideration demographic features, as well as spatial and temporal transit service characteristics. A case study in the city of Charlotte is conducted and a comprehensive gap analysis based on the proposed methodology is performed. Results show that the transit supply provides higher service coverages in the central business district of Charlotte and decreases as the distance to downtown Charlotte increases. Most of the identified transit-dependent areas are suburban and rural communities located on the fringe of the city. A sensitivity analysis is also conducted to examine the impact of different walking distances on transit gaps. Guidelines are developed and best-practice recommendations for the use of GTFS data as a main data source are made to better understand and assess public transit equity and accessibility for public transportation planning and operation. Several policy implications are also drawn in terms of enhancing the public transit system equity and accessibility.

Travel mode identification is one of the key issues in travel behavior analysis. A number of algorithms have been proposed to detect travel modes particularly by using global positioning system (GPS) data, whereas most algorithms rarely consider traffic conditions. To fill the gap, this paper distinguishes two representative travel modes, i.e., bus and car, by using the random-forests method, of which the corresponding feature variables are examined under various traffic conditions. Local congestion variables are defined to reduce uncertainties between bus and car. The results indicate that the overall detection accuracy of the not-in-congestion trips is as high as 94.0%, and that of in-congestion trips is 91.1%, demonstrating that distinguishing traffic conditions using random forests can reliably improve travel modes detection accuracy. It is found that distinguishing local traffic conditions can further improve accuracy. The paper contributes to travel behavior analysis and modeling, and the proposed method is ready for a wide range of transportation practices, including traffic planning and management.

Drivers on the expressway prefer to acquire more traffic information so they can select the best driving route and avoid a traffic jam. Therefore, it is necessary to conduct a study about real-time speed prediction and notify the drivers via their information screen. In this study, the candidate domains of spatial neighborhoods and time windows were first determined considering the spatiotemporal correlation among road sections. Then, a two-dimensional (2D) spatiotemporal matrix for the prediction model was developed. Based on the search characteristics of the nearest neighbor algorithm, we extracted the historical traffic features similar to the current traffic state and reconstructed a training set for each traffic state. Finally, the support vector regression algorithm was used to finish the short-term speed prediction. The case study was conducted using data collected from the expressway of Changchun, China. The space mean speed in each interval was calculated through matching the vehicle information between the two adjacent video detectors. The standard deviation was used to get rid of outliers. After comparison with four other models, the proposed model was proved to have the best performance in single-step as well as multistep prediction.

Vertical curve features on interstate highways greatly affect traffic operations and vehicle performance and, thus, could have an impact on the occurrence of traffic crashes. Most studies to date only considered linear relationships. Though some researchers did consider nonlinearity, the preassumed data distribution may not fit the true distribution perfectly. Thus, the primary objective of this study is to develop a nonparametric algorithm to evaluate the nonlinear correlation between vertical curve features and crash frequency on interstate highways based on a random forest (RF) algorithm. Elevation data along interstate centerlines were extracted from Google Earth for two interstates in Washington State, and 5-year crash data were collected to estimate RF models for crash count prediction. A random effect negative binomial (RENB) model is employed to evaluate predictive performance. Analysis of the variables’ importance shows that the proposed RF models captured the nonlinear correlation between crash count and annual average daily traffic (AADT), the elevation and grade of road segments, median lane width, left shoulder width, ratio of horizontal curve, the standard deviation of grade in 1- and 2-mi road segments, the standard deviation of elevation in 1- and 2-mi road segments, and lane width. Other variables, e.g., right shoulder width and the number of lanes on the highway were also important in the proposed RF models. By better capturing the nonlinearity, the proposed RF model outperformed the baseline model in terms of the predictive performance measurements. The findings of this research can serve to facilitate improvements in highway geometric design and recommend countermeasures to reduce the crash count on interstate highways.

Although a reconfiguration to three-lane with a left turn lane remains the most prevalent cost-effective countermeasure, some four-lane undivided highway segments in Louisiana have been reconfigured to five-lane segments with a left-turn lane by utilizing the existing pavement width basically at the cost of restriping. The current study analyzes nine locations where undivided four-lane roadways were converted to five-lane roadways in Louisiana urban areas with up to 7 years of before-and-after crash data. To avoid any potential regression-to-the-mean bias, the empirical Bayes (EB) method was used with the safety performance function (SPF) developed by the Louisiana Department of Transportation and Development (LaDOTD). Despite the contemplation in design guidelines and previous studies that a five-lane roadway is more crash-susceptible than a four-lane roadway, the safety effectiveness of conversion to five-lane was deduced to be significantly positive. Consistent crash reduction was observed in all sites, which resulted in a crash modification factor (CMF) of 0.48 with a small variance of 0.001. Expectedly, a substantial reduction in the target crash type, rear-end crashes, was achieved. This four-lane to five-lane conversion was found very effective specifically for urban and suburban roadways with annual average daily traffic (AADT) ranging from 15,000 to 30,000 and driveway density from 18 to 31 driveways per kilometer (30–50 driveways per mile). The high safety benefit-cost ratio, $78:1$, indicates strong support to use this countermeasure on four-lane undivided roadways with additional evaluation for feasibility.

The passing zone (PZ) is that part of two-lane highways in which drivers can safely overtake slower vehicles. Several studies have presented passing rate models in the PZ. However, there is no model to predict the passing rate in PZs shorter than 350 m. Furthermore, the effect of variables such as lane width and the proportion of motorcycles on the passing rate were not investigated in previous works. This study assessed the effects of these variables on the passing rate and presents a prediction passing model for short PZs. Data were collected from seven PZs using a drone on three different two-lane rural highway segments in Iran. The results showed that the passing rate depends on the lane width, the absolute vertical grade, the flow rate in both directions, the directional split, the proportion of heavy vehicles in the subject direction, and the proportion of motorcycles in the subject direction. Short PZ length values did not have a significant effect on the passing rate. The passing capacity occurred at a flow rate of $680\mathrm{vehicle}/\mathrm{h}$ in both directions irrespective of the directional split.

In the daily operations of urban rail transit (URT) systems, line capacity sometimes is reduced, which worsens service performance. To measure the effects of line capacity reductions on URT system service performance in a given period, a passenger satisfaction metric and a service quality metric are proposed. The passengers’ generalized travel cost (GTC) is the sum of the monetary value of perceived travel time and travel fare, because perceived travel time can reflect the value of the seat availability and transfer times. When line capacity is reduced, passengers are divided into three categories. Passengers in Categories 1 and 2 still can travel on URT, whereas passengers in Category 3 cannot, due to insufficient capacities on their paths. During reductions in line capacity, the relative GTC stays below a fixed GTC threshold for passengers in Category 1, and above it for passengers in Category 2. Passengers in Category 1 receive acceptable service because their GTC increases only slightly. The fraction of passengers in Category 1 and the average GTC per passenger in Categories 1 and 2 in a given period are defined as passenger satisfaction and service quality metrics, respectively. The system service performance in Wuhan, China’s URT during morning peak periods and the effect of providing discount fares on system service performance were computed for separate capacity reductions in each rail line. The results show that the proposed indicators and methods can identify the critical lines that greatly affect URT system service performance. The proposed indicators also can identify the number of lines with reduced capacity and the capacity reduction ratios that a URT system can withstand while maintaining particular system service levels. Providing discount fares is an effective way to improve URT system service performance when a line’s capacity is reduced slightly.

Most universities limit the number of student parking permits and use their prices to moderate the parking demand. Two critical inputs for implementing such a practice are: (1) the total demand (number of students who will purchase parking permits in a 12-month academic year); and (2) the single, campus-wide base price of student parking permits, benchmarked against other universities. Parking, demographic, and economic statistics were gathered from 208 universities across the United States to calibrate and validate a Beta regression total demand model and a Tobit regression base price model. Three case studies are then presented to illustrate the applications of the models. The first case study compared the models’ solutions against parking management practices at four random selected university campuses; whereas the second case study applied the models to a university to forecast the total demand and the base price in the future. In the third case study, the Monte Carlo simulation technique was adopted to handle uncertainties in the independent variables in the forecasts.

Congestion on freeways occurs when demand exceeds the available capacity. Common causes of recurring congestion, also known as freeway bottlenecks, include lane drops, on-ramp merges, and weaving sections. Adverse weather can reduce the maximum queue discharge flow, but this effect has not been systematically investigated. This research examined the relationship between discharge flow and weather characteristics including rainfall intensity, wind speed, and visibility. Queue discharge rates at four isolated merge bottlenecks were measured using an established methodology of cumulative count and occupancy curves. An analysis of discharge variation by rainfall intensity revealed reduced discharge ranging from 5% in drizzle (rainfall $<0\mathrm{in.}/\mathrm{h}$) to 27% in heavy rainfall [rainfall $>2.54\mathrm{m}\mathrm{m}/\mathrm{h}$ ($>0.1\mathrm{in.}/\mathrm{h}$)]. However, rain intensity accounts for only a portion of the variability in discharge flow. Two hypotheses were tested using the additional variables of wind speed and visibility as well as dividing the periods of discharge flow into multiple groupings. Analyses based on these hypotheses described the variation in queue discharge flow better than the analysis with a single independent variable. This research showed that weather characteristics are an important predictor of bottleneck queue discharge rates.

For a specific multiobjective tunnel alignment design problem, engineers have difficulty finding a suitable solution to satisfy a set of preference criteria. This paper presents a biobjective optimization approach incorporating the preference information to the search process interactively, which can greatly accelerate this process and guide the search to the relevant Pareto region. Safety performance and construction cost are cast in the model as two conflicting objective functions. Moreover, a case study in Shanghai, China that applied the biobjective optimization model was conducted to demonstrate the capability of our new approach compared with traditional alignment design models. The results indicated that the incorporation of preference could not only provide a more qualified solution set than traditional alignment optimization models, but also save computation time as well as the engineers’ workload to compare and select alternative solutions.

In previous studies, almost all urban network design models/methods made attempts to optimize transportation system performance, such as minimizing road traffic congestion, by determining optimal road capacity expansion schemes, with the assumption of indestructible road infrastructure. However, road infrastructure definitely deteriorates over time, and needs periodic maintenance and repairs. This paper proposes a bilevel programming model for the urban freight transportation network design problem (UFTNDP) by taking into account the heterogeneity of road classifications and road damages from the perspective of road maintenance. The model aims to improve network performance as much as possible by determining an optimal joint scheme of road capacities and classifications. At the upper level, road planning aims to minimize total system cost by determining a joint optimal scheme of road capacities and road classifications. The lower level is a traffic assignment problem that characterizes network users’ path choice behavior. We then propose an improved system optimum (SO) relaxation–based method to solve the optimization model. Numerical examples validated the developed model and tested the computational efficiency of the proposed algorithm. Numerical results revealed that (1) the improved solution algorithm performs better, because it saves 97.3% of computational time compared with the original SO relaxation–based method; (2) the algorithm has the potential to be extended to solving a general discrete network design problem; and (3) a simultaneous optimization of road classification and capacity works better in improving system performance of an urban freight transportation network.

Pedestrian crash data are not available in sufficiently large quantity and suffer from known problems such as a low-mean small sample, underreporting, and misclassification. Moreover, under heterogeneous traffic conditions, due to the involvement of multiple classes of vehicles, pedestrian–vehicle interactions become even more complex. To address this limitation, a pedestrian risk index (PRI) linking the probability of a crash between vehicle and pedestrian and the severity of the conflict was developed for varying road and traffic conditions. Data were analyzed from nine locations in different parts of India, accounting for variations in geographical distribution and pedestrian–vehicle interactions. The derived values of PRI were assessed by vehicle type and road geometry. The PRI value was significantly higher when the approaching vehicle was three-wheelers (3W), two-wheelers (2W), and cars compared with heavy vehicles such as buses and trucks. This indicates that the severity of conflicts is higher for lighter vehicles. Furthermore, the addition of lanes increases the PRI value. As an important outcome, variation in PRI values was modeled as a function of vehicle speed, pedestrian volume, and vehicle volume using a multilinear regression approach. The developed model can enable planners and engineers to compute PRI using the independent variables and to evaluate pedestrian safety at urban midblock crossings. Overall, this research contributes immensely to assessing the prevailing level of safety at crosswalks under heterogeneous traffic conditions, thereby improving pedestrian safety.