Journal of Transportation Engineering, Part A: Systems

Estimation of passenger car unit (PCU) values is very important for traffic capacity analysis and other relevant applications such as determining the saturation flow rate and signal design. However, the usual methods are not completely analogous for mixed traffic due to the presence of a wide variety of vehicle types, poor lane discipline, variability of intraclass vehicles. The parameters required for PCU estimation under mixed traffic are relatively more complex to measure, causing a severe data limitation. The accurate estimation of PCUs relies on the collection of sufficient amounts of data, and unmanned aerial vehicles (UAV) like drones are used in recent years by researchers as they significantly reduce efforts of data collection and extraction. This paper illustrates three methods for PCU estimation under mixed traffic on two signalized intersections of China using UAV monitoring data. The first method is based on the travel time of a vehicle while clearing the intersection. The second method is based on the average speed estimated in terms of different vehicle categories. The lagging headway ratio is used as the basis for PCU estimation in the third method. The author explores the effect of the driving direction and compared the PCU values at different time periods. The results show that the PCU value of vehicles turning left is always greater than that of going straight. The usage of a combined parameter is more appropriate than a single parameter in mixed traffic. The values are similar to those of India but are quite different from the United States. The values obtained in this study can be used as a guideline in the traffic analysis at signalized intersections in China and generally in other developing countries.

To achieve more-accurate traffic simulation and autonomous vehicle control, increasing attention recently has been devoted to the analysis of car-following behavior considering leader–follower vehicle types. Firstly, on the basis of a vehicle trajectory data set, distance headway (DHW) and time headway (THW) distributions of leader–follower vehicle type combinations, including car following car (CC), car following truck (CT), truck following car (TC), and truck following truck (TT), were compared to verify their significant differences. Then best-fit distribution models of four combinations were identified in each scenario. Next, characteristics of DHW and THW of four combinations in different speed ranges were determined according to their best-fit distribution models. Finally, the transferability of the results was verified. Results show that DHW and THW of CC and CT can be modeled as log-logistic distributions, and DHW and THW best-fit models of TC and TT are gamma distributions. CF behavior depends more on the following vehicle type when the speed is less than $26\mathrm{m}/\mathrm{s}$, and the impact of the leading vehicle gradually occurs when the speed is higher than $26\mathrm{m}/\mathrm{s}$.

This study seeks to design a city-level trip speed performance index (CTSPI) providing an alternative aspect in quantifying the traffic performance of an entire city. Another objective is to disentangle the original CTSPI time series into several featured patterns, including a trend pattern, two seasonality patterns, and a remainder pattern. The big data in the form of observations of ride-sourcing trips in Beijing, China were adopted. This study also introduces a state-space model, the TBATS [trigonometric, Box–Cox transformation, auto-regressive moving average (ARMA) errors, trend, and seasonal components] filtering procedure to decompose the CTSPI time series. This study adopts Beijing as a representative example because the city has very typical and complicated traffic performance patterns. The proposed CTSPI directly reflects the average trip speed, normalized by the best performance supplied by the corresponding infrastructure systems. After filtering out fluctuation, noise, and irregular patterns, it reveals a smooth and clear-cut trend in the evolving process of the city’s traffic condition, which was never previously disclosed and is important in understanding the macroscopic long-term tendencies of the city’s traffic performance. The results indicate that the CTSPI is capable of capturing the traffic performance of the city well and can sense the influence of special dates or major events, such as the Beijing 2022 Olympic Winter Games, advising tremendous application of traffic management. City-level macroscopic traffic performance is usually measured as index quantities and used to assess traffic situations in different cities. Most often, it is utilized to provide a quantified impression of the degree of congestion to the public, or as traffic-congestion criteria for ranking cities. This study illustrates the importance of measuring city-level macroscopic traffic performance, especially on a daily basis as is appropriate for gauging the impacts of many macroscopic factors on city-level traffic situations.

Connected and automated vehicles (CAVs) will potentially improve transportation system performance. Guidance on the capacity impact of CAVs at different market penetration rates (MPRs) will help agencies to incorporate the effects of CAVs when planning and designing roadways. Traditionally, practitioners have used the Highway Capacity Manual (HCM) to assess capacity and evaluate the quality of service for various facilities and systems. Although several studies have provided insight into the capacity benefits of CAVs, there is a need for quantified CAV effects that can be used to develop HCM guidance. In this study, the capacity benefits of CAVs at signalized intersections were estimated, and capacity adjustment factors (CAFs) were developed for the HCM. The researchers considered variations in CAV gap/headway settings, platoon lengths, turning movement types (through and left), and left-turn phasing modes (protected versus permitted). Microscopic traffic simulation was used to model CAVs. The results showed that performance indicators such as saturation headway gradually improved with increases in CAV MPR, resulting in up to 40% capacity increase at 100% MPR for the protected movements. For permitted left turns, up to 45% capacity increase could be achieved at 100% MPR. This increase in permitted left-turn capacity can be attributed to vehicle-to-vehicle (V2V) communication, which provides advanced information on available gaps in conflicting traffic and reduced follow-up headway time both for permitted left turns and the opposing through movement. Based on the capacity results, this study provides CAF tables for CAVs that can be easily integrated into the HCM and used for planning-level guidance by practitioners.

Emerging sources of mobile location data such as Strava and other phone-based apps may provide useful information for assessing bicycle activity on each link of a network. Despite their potential to complement traditional bike count programs, the representativeness and suitability of these emerging sources for producing bicycle volume estimates remain unclear. This study investigates the challenges and opportunities by fusing Strava data with short-term and permanent conventional count program data to produce bicycle volume estimations using clustering and nonparametric modeling. Analysis indicates that the concentration of permanent counters at high bicycle volume locations presents a significant challenge to produce network-wide daily volume estimations even though Strava data demonstrate potential in mitigating the estimation bias at lower-volume sites. Despite the contribution of Strava to develop reliable and spatially and temporally transferable bicycle volume estimations, significant challenges remain to rely on Strava counts alone to characterize network-level activities due to sampling bias and spatial representations. This study will help planners discern and assess the challenges and opportunities of using emerging data in bicycle planning.

This paper introduced a new intersection design, the double contraflow intersection (DCI), that could help at big, busy suburban sites, and explored how well it would work. The DCI uses contraflow left turn lanes on each approach to minimize the conflicting traffic flows. The DCI has the potential to provide slightly better capacity than the continuous flow intersection (CFI) and median U-turn (MUT), and much better capacity than a comparable conventional intersection, while mitigating some of the known limitations of the CFI and MUT. Our results showed that the DCI was generally superior to the CFI, MUT, and conventional designs in capacity and travel time. The DCI likely looks about as safe as the conventional and CFI designs, and is as good in through progression as the CFI. The DCI looks to be generally behind the MUT in pedestrian service, and behind the CFI in bicyclist service, but not by much in either case. The DCI looks to be generally behind the conventional design, but better than the CFI, in driveway service and right-of-way needed. In sum, the DCI looks like it could have a future in helping relieve traffic congestion at some suburban intersections and deserves attention from researchers and project teams.

The creep camber of box girders significantly lowers track regularity, making it very important to perform track geometry precise adjustment or ballast tamping operations by maintenance machinery on box girder sections at the right timing. The appropriate maintenance operation should be conducted after the creep camber reaches a stable state. To address this challenge, in this study, power and frequency spectra were used to identify the typical creep camber section of 32-m box girders on a railway line based on the inspection data of high-speed track geometry cars. Also, wavelet decomposition, moving average filtering, and the length error minimization principle were adopted to position both ends of each girder. The Mann–Kendall trend test was conducted to determine whether the creep camber reached a stable state, and ARIMA (1,1,1) was employed to predict track surface values at the ends of box girders with still-developing creep camber. The validity and reliability of the proposed framework were verified using the inspection data on the Beijing–Shanghai high-speed railway track $\mathrm{K}313+84\sim \mathrm{K}691+895$ during the period of 2012–2016. The results show that the proposed framework can accurately identify the typical creep camber section based on inspection data and precisely position both ends of a 32-m box girder. The determined creep camber states were consistent with the manually obtained results, and the proposed method can predict track surface values at the girder ends for the next 5 months more accurately than the long short-term memory networks (LSTM) and supported vector machine (SVR).

The present study aims to develop vehicle type-wise operating-speed prediction (OSP) models for heterogeneous traffic on two-lane rural highways in Indian hilly terrains. For the present study, 27 curves with varying geometric characteristics located along the National Highway (NH-953) connecting Netrang and Rajpipla in the western state of Gujarat, India, were selected. Speed data were collected using radar guns at three curve locations (entry point, midpoint, and exit point) in each travel direction for three dominant types of vehicles: motorized two-wheelers (2W), cars, and heavy commercial vehicles (HCVs). OSP models were developed for different vehicle types at three curve points using the backward elimination stepwise regression (BSR) technique. The results revealed that the preceding curve point’s operating speed, curve length, and tangent length positively affected operating speed. In contrast, deflection angle, curve sharpness, and grade had adverse effects. The curve geometric characteristics had the most negligible impact on the operating speed of 2W and a significant effect on HCV. Among all the curve-related aspects, curve length was the most significant variable and affected the speed of all three vehicle types, followed by curve sharpness. Further, the developed OSP models were applied to the other hilly terrain to check the transferability of the model. As an important outcome, the developed OSP models were used to evaluate geometric design consistency. This highlights the need for geometric and traffic-calming measures to improve highway operating-speed consistency and driver safety.

This study introduces a multiobjective optimization framework for the redimensioning of the cross-sectional elements of rural horizontal curves with limited sight distance. The optimization aims at minimizing both the risk associated with the limited sight distance and the expected collision frequency corresponding to the cross-sectional elements’ dimensions. The risk component was assessed using an index known as $P_{nc}$, which is developed based on reliability theory using the First-Order Reliability Method (FORM). The change in collision frequency corresponding to the change in the cross-sectional elements was extracted from the literature. The risk and the safety components were then combined into one measure, a combined crash modification factor (${\mathrm{CMF}}_{\mathrm{combined}}$), to develop a direct measure of the safety impacts of the optimization. The proposed framework was applied to five restricted curves in British Columbia, Canada, considering various scenarios. The results showed a considerable reduction in the $P_{nc}$ value (ranging from 12% to 73%), the expected collision frequency (ranging from 10% to 31%), and the estimated combined collision reduction ${\mathrm{CMF}}_{\mathrm{combined}}$ (ranging from 48% to 76%). The framework presented in this study would support transportation engineers in selecting optimal dimensions of cross-sectional elements of restricted horizontal curves, understanding the safety consequences of selecting a specific cross-sectional configuration, and assessing the economic viability of different design options.