International Journal of Transportation Science and Technology最新文献

This paper proposes a possible methodology for detecting and mitigating traffic congestion. This method is carried out using a custom-designed traffic scenario model. The model is fully developed in lieu of abundant data support from actual traffic events, which is applicable to localized traffic surveillance conditions, where massive data collection from surveilling devices is infeasible or unviable. This approach includes two parts: model construction and re-routing strategy. The model construction part focuses on the development of a traffic driving scenario, which takes various criteria such as traffic volume and traffic signal into consideration. The goal of this setup is to create a realistic-possible environment, where the proposed methods can be tested. The re-routing strategy is implemented based on the model simulation result of a medium-scale drive-able road map. The idea of the adaptive vehicle re-routing strategy is inspired by the k-shortest path algorithm, adapted with the dynamic congestion re-routing strategy. It will be shown that the model is able to automatically identify congestion patterns that are happening on any road segments, and then initiates a proper re-routing strategy to alleviate such congestion in a timely manner. Although the methodology is realized and validated within a simulated model, the concept is transparent to any transportation system under study without extra complexity. In addition, the proposed modeling and simulation technique can be used for real-time implementation in intelligent transportation management systems.

Research on grocery shopping channel preferences has been growing in the wake of the Covid-19 pandemic. However, few studies have utilized the discrete choice experiment (DCE) to elicit choices in hypothetical scenarios. Moreover, attitudinal factors, which may better explain preference heterogeneity, are rarely considered. Given that the evolution of shopping behavior in the context of the Covid-19 pandemic has huge implications for transportation planning and modeling, this study aims to examine consumers’ grocery shopping channel preferences through a DCE that was constructed with three grocery shopping channels (home delivery, curbside pickup, and in-store) and five time–cost attributes (product price, shopping time, delivery time, delivery cost, and travel time). 8 603 responses were elicited from 1 229 Florida residents between February and April 2021. Information on various aspects of respondents’ shopping attitudes as well as their socio-demographic and household attributes, grocery shopping activities, and distance to the grocery store were also collected. Using mixed logit modeling for analyses, results indicate that individuals with low education, in low- to middle-income earning households, with three or more household vehicles, and having full access to a vehicle tended to prefer in-store shopping. Also, perceived security risk, pro-alternative mobility options, pro-local store shopping, and shorter distances to grocery stores predisposed individuals toward in-store shopping. Alternatively, females, young and middle-aged individuals, workers, and individuals in large households tended to prefer home delivery and curbside pickup. Technology savviness, pro-environment, pro-online shopping, and shopping enjoyment were also drivers of home delivery and curbside pickup purchases, while cost and time consciousness did not show significant effects. Overall, the findings in this study have implications for retailers, transportation planners, and policymakers.

It is interesting that despite its long-term and widespread use in China, relatively little is known about the operational characteristics of a variable approach lane (VAL) in real world. Using one month of inductive-loop detector data at ten dynamic approaches (intersection approaches with dynamic lane assignment) from different intersections in Hangzhou, China, this paper presents the results of a study materializing the flow characteristics of variable approach lanes by comparing them with adjacent normal-flow lanes under various operating conditions. The effectiveness of the results was examined in a case-control analysis by integrating 12 fixed approaches (without variable lane) as benchmark. It was found that the difference or similarity of flow rate between the variable lane and the normally-flowing lane differs under a variety of traffic volume, time-of-day, mode-of-operation, and overhead lane-use guidance sign (OHS) location conditions. The study also revealed that while naturally there may be a difference in the flow rates between referencing lanes at fixed approaches, the flow difference percentage (FDP) at dynamic approaches is significantly higher.

Emulsified asphalt/polymer is one of the most applied composite materials in pavement industry, which is also defined as emulsified polymer modified asphalt (PMA). PMA exhibits the transition behavior of multi-physical states with corresponding viscoelastic characteristics, and studies had focused on the application performances of PMA. However, few had paid proper attention to the morphological compatibility of PMA, which dominated its viscoelastic transition and failure probability directly. This paper aims to establish a gray-level co-occurrence matrix (GLCM) model with morphology theory to quantitatively describe the morphological compatibility of PMA, combined with fluorescent microscope. Furthermore, waterborne polymers with self-crosslinking characteristics are introduced to modify emulsified asphalt, including acrylate, epoxy resin, polyurethane and liquid rubber. The rheological characteristics of PMA are comprehensively evaluated through performance grade, steady shear viscosity, and master curve tests. Finally, a statistical analysis is conducted to establish the relationship between morphological compatibility and rheological characteristics of PMA. The approach introduced in this study could be a promising method to investigate the multi-physical transition behaviors and morphological compatibility of emulsified asphalt/polymer composite material.

Automated vehicles (AVs) hold great promise for creating a safer, more efficient, more equitable, and more sustainable transportation system. However, the rapid adoption of AVs requires a thorough understanding in their coexistence with the human environment in the current roadway network, particularly with respect to interactions between AVs and non-motorists. Bike Pittsburgh (BikePGH) conducted a 2019 survey to examine non-motorists' perceptions of AV safety. Using Bayesian network (BN) analysis, the study identified key factors such as safety perception, AV technology knowledge, and real-world interaction experiences that influence non-motorists' overall perception of AV safety using BikePGH survey data. The study also explored several counterfactual scenarios to gain insights into non-motorists' viewpoints on AV safety. Notably, the study found that the differences in the ways of AVs and human-driven vehicles interacted with non-motorists at intersections played a crucial role in shaping survey participants' opinions. By taking into account the key insights identified in this study, policymakers can develop evidence-based strategies to achieve sustainable urban mobility goals while ensuring the safety and well-being of all road users, particularly non-motorists.

Pedestrian crashes at high-speed locations are a persistent road safety concern. Driving at high speeds means that the driver has less time to react and make evasive maneuvers to avoid a pedestrian crash. On top of this, other crash-contributing factors such as humans (pedestrians or drivers), vehicles, roadways, and surrounding environmental factors actively interact together to cause a crash at high-speed locations. The pattern of pedestrian crashes also differs significantly according to the high-speed intersection and segment locations which require further investigation. This study applied association rules mining (ARM), an unsupervised learning algorithm, to reveal the hidden association of pedestrian crash risk factors according to the high-speed intersection and segments separately. The study used Louisiana pedestrian fatal and injury crash data (2010 to 2019). Any crash location with a posted speed limit of 45 mph or above is classified as a high-speed location. Based on the generated association rules, the results show that pedestrian crashes at a high-speed intersection are associated with the intersection geometry (3-leg) and control (1 stop, no traffic control device), driver characteristics (careless operation, failure to yield, inattentive-distracted, older, and younger driver), pedestrian-related factors (violations, alcohol/drug involvement), settings (open country, residential, business, industrial), dark lighting conditions and so on. Most pedestrian crashes at high-speed segments are associated with roadways with no physical separation, dark-no-streetlight conditions, open country locations, interstates and so on. The findings of the study may help to select appropriate countermeasures to reduce pedestrian crashes at high-speed locations.

This article presents a methodology to estimate the entry capacity (EC) and total capacity (TC) of basic turbo roundabouts under partial and fully connected and autonomous vehicle (CAV) environments. EC calculations are partially based on capacity models and adjustment factors proposed by the HCM 7th edition, taking into account different proportions of CAVs in traffic streams. The proposed methodology was applied to a case study concerning a basic turbo roundabout with different traffic demands and market penetration levels (MPLs) of CAVs. It was assumed that the traffic stream consisted of 100% passenger cars with MPLs of CAVs ranging from 0% to 100%. The research proves that with the increase in MPLs of CAVs, ECs increase accordingly and delays and queues decrease. To maximize the TC, a control area was also hypothesized, where CAVs start to communicate with a turbo roundabout manager system. The system should be able to distribute and channel CAVs, and therefore the entering flows between entry lanes find the values of the maneuver distribution factors (α, β, γ, δ) between the right lane and the left lane of entries to maximize the TC for each origin–destination matrix of traffic flows.

In this paper, a meaningful representation of the road network using multiplex networks and a novel feature selection framework that enhances the predictability of future traffic conditions of an entire network are proposed. Using data on traffic volumes and tickets’ validation from the transportation network of Athens, we were able to develop prediction models that not only achieve very good performance but are also trained efficiently, do not introduce high complexity and, thus, are suitable for real-time operation. More specifically, the network’s nodes (loop detectors and subway/metro stations) are organized as a multilayer graph, each layer representing an hour of the day. Nodes with similar structural properties are then classified in communities and are exploited as features to predict the future demand values of nodes belonging to the same community. The results reveal the potential of the proposed method to provide reliable and accurate predictions.

Road pavement surfaces need routine and regular monitoring and inspection to keep the surface layers in high-quality condition. However, the population growth and the increases in the number of vehicles and the length of road networks worldwide have required researchers to identify appropriate and accurate road pavement monitoring techniques. The vibration-based technique is one of the effective techniques used to measure the condition of pavement degradation and the level of pavement roughness. The consistency of pavement vibration data is directly proportional to the intensity of surface roughness. Intense fluctuations in vibration signals indicate possible defects at certain points of road pavement. However, vibration signals typically need a series of pre-processing techniques such as filtering, smoothing, segmentation, and labelling before being used in advanced processing and analyses. This research reports the use of noise-cancelling and data-smoothing techniques, including high pass filter, moving average method, median, Savitzky-Golay filter, and extracting peak envelope method, to enhance raw vibration signals for further processing and classification. The results show significant variations in the impact of noise-cancelling and data-smoothing techniques on raw pavement vibration signals. According to the results, the high pass filter is a more accurate noise-cancelling and data smoothing technique on road pavement vibration data compared to other data filtering and data smoothing methods.

Left turn traffic at unsignalized T-intersection on undivided rural two-lane high-speed highways poses both operational and safety challenges. More complexities are faced by through drivers in the same direction as the stopped or slowed down left-turn vehicle must choose to either slow down and wait or bypass the left-turn vehicle. Therefore, this study intends to explore the operational characteristics of these facilities. The focus is on the reaction of the drivers behind the left-turn vehicle in terms of the types of maneuvers taken to avoid collision and the distance upstream for the evasive maneuvers using field observations. Further, the impact of the drivers’ reaction on the intersection delay is assessed using a simulation analysis of 17 generic 10.5-mile two-lane corridors with varying configurations of passing lanes at or near the intersection with and without a left-turn lane. The field observation findings from five sites reveal that drivers will move to the shoulder to avoid slowing and stopping or colliding with the left-turn vehicle. The distance at which drivers move to the shoulder differs for the sites studied. The simulation results show that a relatively similar magnitude of reduction in intersection delay could be achieved by addition of either passing lane or left-turn lane, such addition is beneficial for at least 17 000 vpd intersection volume where the passing lane does not end within 1 500 ft is downstream of the intersection. The findings are expected to improve traffic operations at T-intersections on rural two-lane highways.