International Journal of Transportation Science and Technology最新文献

Motor vehicle crashes are the leading cause of the death of teenagers in the United States. Young drivers have shown a higher propensity to get involved in crashes due to using a cellphone while driving, breaking the speed limit, and reckless driving. This study analyzed motor vehicle crashes involving young drivers using New Jersey crash data. Specifically, four years of crash data (2016–2019) were gathered and analyzed. Different machine learning (ML) methods, such as Random Forest, Light GBM, Catboost, and XGBoost, were used to predict the injury severity. The performance of the models was evaluated using accuracy, precision, and recall scores. In addition, interpretable ML techniques like sensitivity analysis and Shapley values were conducted to assess the most influential factors' impacts on young driver-related crashes. The results revealed that XGBoost performed better than Random Forest, CatBoost, and LightGBM models in crash severity prediction. Results from the sensitivity analysis showed that multi-vehicle crashes, angular crashes, crashes at intersections, and dark-not-lit conditions had increased crash severity. A partial dependence plot of SHAP values revealed that speeding in clear weather had a higher likelihood of injury crashes, and multi-vehicle crashes at the intersection had more injury crashes. We expect that the results obtained from this study will help policymakers and practitioners take appropriate countermeasures to improve the safety of young drivers in New Jersey.

Current vehicle carbon emission models tend to ignore the influence of road roughness on driving speed selection, which may damage the carbon emission evaluation accuracy. In this study, first, based on the results obtained with a portable emissions measurement system (PEMS), an explicit model for user vehicle carbon emissions, driving speed, and pavement roughness is established. Second, the influence of road roughness on driver behavior choice is investigated, and an interrelationship model between roughness and driving speed choice is developed. Finally, a more realistic carbon emission calculation model during the operation period is proposed based on the pavement performance model, and the accuracy is verified in comparison with the traditional vehicle operating cost (VOC) model. It is found that there exists a carbon emission minimization point under free-flow conditions, and the corresponding driving speed is the optimal speed point of user vehicles, i.e. 63 km/h. In addition, a great linear correlation exists between the roughness and driving speed selection, which should be considered in the final calculation model. The vehicle carbon emission model developed in this research provides solid references for evaluating the life-cycle emission of asphalt pavement and guiding the selection of maintenance strategies for the pavement to lower carbon emissions.

Freeway work zone forms as a result of traffic crash or road rehabilitation. To ascertain the effects of work zone with lane II completely blocked on vehicular flow on ring freeways with a tunnel, a three-lane continuum model is put forward. The mandatory net lane-changing rate from lane II to lane I or III just upstream of the work zone is described by a random number model, with the random number being produced within a small range around a median based on a golden section analysis. The net-changing rate between adjacent lanes is described using a lane-changing time on the basis of an assumption: the time ratio to relaxation time equals infinity when the absolute value of traffic densities between the two adjacent lanes is less than 1 veh/km, implying that the net-changing rate is zero; otherwise, the time ratio is inversely proportional to the vehicular spatial headway, which is equal to unity for traffic flow at saturation state, but infinity when the traffic flow is completely jammed. It is assumed that the freeway is a three lane ring with a total length of 100 km, and has a tunnel with a speed limit of 60 km/h and a length of 1.6 km located downstream the work zone with a length of 0.16 km. The free flow speeds on lanes I, II, and III are 120 km/h, 100 km/h, and 85 km/h, respectively. For the vehicular flow on the ring freeway with a tunnel, numerical simulations based on the three-lane continuum model are carried out with a reliable numerical method of high accuracy. It is found that the vehicular flow has two thresholds of traffic jam formation, one depending upon the tunnel and the other upon the work zone. The tunnel triggers a traffic jam when the initial density normalized by jam density is equal to the first threshold 0.15, and the work zone originates another traffic jam when the normalized initial density equals the second threshold 0.19. The freeway tunnel plays a dominant role in the prediction of mean travel time as soon as the tunnel has generated a traffic jam at the tunnel entrance. For the vehicular flow at unsaturated state, the average speed through the tunnel is about 26.67 km/h. When the normalized initial density exceeds the second threshold 0.19, the mean travel time through every lane increases with the initial density linearly. Vehicle fuel consumption can be estimated by interpolation with the time averaged grid traffic speed and an assumed vehicle performance curve. It is found that the vehicle fuel consumption is lane number dependent, and distributes with the initial density concavely, as well as has a value in the range of 6.5 to 8.3 l.

Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure. Although conventional algorithms for periodic frequent pattern detection have numerous applications, there is still little research on periodic frequent pattern detection of individual passengers in the metro. The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network, which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data. This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes (PFPTS). This discovered pattern can automatically capture the features of the temporal dimension (morning and evening peak hours, week) and the spatial dimension (entering and leaving stations). The corresponding complete mining algorithm with the PFPTS-tree structure has been developed. To evaluate the performance of PFPTS-tree, several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network. The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.

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.

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.