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Host cardinality estimation is one crucial task in network traffic measurement. Super host is the host that exhibits anomalies in host cardinality and it is usually related to network abnormal events. Therefore, accurate host cardinality estimation is the premise of super host identification and it can be used to measure the trust degree of a network. Sketches have been widely used in super host identification. However, expanding network scale and increasing link rate bring challenges to the efficiency and accuracy of sketch-based super host identification. The size of the counters in most sketches requires a trade-off between memory resources and accuracy. To meet the need of measuring high cardinalities, large size counters are applied, which leads to memory waste in monitoring low cardinality hosts. On the other hand, most sketches have high computational overhead when tracking superhosts, resulting in inefficiency. In order to address these issues, we propose a novel memory efficient and reversible sketch, named ExtendedSketch+, to provide accurate host cardinality estimation with the purpose of super host identification and network host trust evaluation. ExtendedSketch+ achieves both high memory utilization efficiency and high accuracy, in addition to high super host identification efficiency. It applies extensible counters to record unbalanced host cardinality distribution, by adaptively expanding the counters with the increase of cardinality. It adopts a lossless traffic information transfer strategy during counter extension to ensure the accuracy of cardinality estimation. It can directly tracks the host with the highest cardinality in each bucket, which greatly improves the identification efficiency of super hosts. Based on accurate cardinality estimation of ExtendedSketch+, we can further accurately evaluate host trust degree. We theoretically analyze ExtendedSketch+ with regard to its space and time complexities and estimation accuracy. We conduct performance evaluation based on real network traffic traces. Experimental results show that ExtendedSketch+ performs better than state-of-the-art sketches regarding super host identification and provides accurate host trust evaluation. • A memory efficient and reversible sketch named ExtendedSketch+. • A quick and accurate super host identification method based on ExtendedSketch+. • An accurate network host trust evaluation method based on ExtendedSketch+. • A detailed theoretical analysis on ExtendedSketch+ on complexities and accuracy. • A performance comparison to show the superiority of ExtendedSketch+. [ABSTRACT FROM AUTHOR]

The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse. The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 and 2019. We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion. In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night. Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures. • We assessed traffic congestion in an environmental justice community over 2 years. • We analyzed time series of crowd-sensed traffic maps. • Traffic increased in the South Bronx in New York City between 2017 and 2019. • The local community will use the data to advocate for mitigating measures. • Crowd-sensed traffic data allow assessing community impacts of traffic globally. [ABSTRACT FROM AUTHOR]

Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big-volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions. • Main traffic measurement tasks and advantages of applying sketches. • Criteria to evaluate sketch-based network traffic measurement methods. • Serious review on the state-of-art sketch-based traffic measurement. • Significant open issues and promising future research directions. [ABSTRACT FROM AUTHOR]

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection. • We show the utility of using adjacent traffic sensor measurements to reduce TTDA. • XGBoost classifier produces the best results as quickly as 1.0 min. • XGBoost classifier produces the best results (AUC-ROC 83% and AUC-PR 49%). • We show that features of both upstream and downstream sensors are the most important. • We report results on two different datasets (i.e., I-75 and I-24) in Chattanooga, TN. [ABSTRACT FROM AUTHOR]

Maritime Traffic Situation (MTS) is crucial for enhancing the efficiency and safety of maritime navigation, with the complexity of ship traffic serving as a key aspect of identifying and managing maritime risks. This paper proposes a ship traffic complexity measurement model for MTS. The proposed model quantifies the complexity between ships by evaluating factors such as density, proximity factor, and mitigation index, and then integrates these evaluations into multiple ships interaction scenarios to enable a comprehensive assessment of ship traffic complexity. The results of case study have fully demonstrated the model's capability in identifying potential collision risks during the interaction processes of multiple ships situation within a navigational sea. Research on the ship traffic complexity measurement model can provide strong theoretical and practical support for maritime traffic management and navigational safety. • Building on an in-depth exploration of the intrinsic attributes of ships, this paper introduces ship domain (SD) into the calculation of proximity factor within the ship traffic complexity measurement model. By constructing different SDs for different ships, the model can more accurately assess collision risks. • Incorporating the mitigation index, based on ships' speed and course parameters, into the ship traffic complexity measurement model enables the quantification of ships' movement trends within specified temporal and spatial domains. This integration allows the model to accurately assess the mitigation complexity of ships in various MTS, taking into account their dynamics. • Expanding the ship traffic complexity measurement model from focusing on pairs of ships to encompassing the quantification of traffic complexity in multi-ship interactive scenarios has significantly enhanced the model's practical application value. [ABSTRACT FROM AUTHOR]

• The ability of ISS and NISS in predicting multiple injuries to a rider is compared. • Discriminatory ability of ISS and NISS in predicting clinical outcomes is examined. • Compared with the NISS, the ISS underestimated the actual injuries. • ISS and NISS are correlated with higher odds of mortality and trauma risk. • NISS exhibits a better discriminatory ability in predicting clinical outcomes. Introduction: Little evidence exists in the literature regarding the discrimination power of better anatomical injury measures in differentiating clinical outcomes in motorcycle crashes. Furthermore, multiple injuries to different body parts of the rider are seldom analyzed. This study focuses on comparing anatomical injury measures such as the injury severity score (ISS) and the new injury severity score (NISS) in capturing injuries of multiple injured riders and examining the discriminatory capabilities of the ISS and NISS in predicting clinical outcomes post motorcycle crash. Methods: The study harnessed unique and comprehensive injury data on 322 riders from the US DOT Federal Highway Administration's Motorcycle Crash Causation Study (MCCS). Detailed exploratory analysis is performed and discrete/ordered statistical models are estimated for three clinical outcomes: mortality risk, trauma risk, and trauma status. Results: Around 9% of the riders died and 45% of the riders had injuries. Around 36% of the riders were hospitalized, disabled, or institutionalized. While a very strong dependence was found between ISS and NISS, ISS underestimated injuries sustained by riders. Statistical models for mortality risk revealed that a unit increase in the ISS and NISS was correlated with a 1.18 and 1.17 times increase in the odds of mortality, respectively. Moreover, a unit increase in ISS and NISS values was correlated with a higher trauma risk by 1.48 and 1.36 times, respectively. Our analysis reveals that the probability of a rider being hospitalized or disabled/institutionalized increases with an increase in the NISS. Conclusions and practical applications: The NISS exhibits significantly better calibration and discriminatory ability in differentiating survivors and non-survivors and in predicting trauma status – underscoring the importance of accounting for microscopic body-part-level injury data in motorcycle crashes. We consider that compared with the KABCO scale, the ISS and NISS are more nuanced scores that can better measure the overall injury intensity and can lead to more targeted countermeasures. [ABSTRACT FROM AUTHOR]

• Queue length estimation models for connected vehicles are developed. • Revised models are developed when range measurement sensor are present. • Models with fusion are evaluated using microscopic traffic simulations and field data. • Knowing back of queue improves the accuracy as much as 25% over without sensor. • Accuracy was improved especially at low market penetration rates up to 30%. This paper presents a method for queue length estimation from connected vehicles equipped with range measurement sensors such as light detection and ranging, stereo camera, and ultrasonic. Simple plug-and-play models are developed for queue length estimations that do not require ground truth queue lengths. The proposed method is simple to implement and can be adopted to cyclic queues like traffic signals with known phase lengths. The models are evaluated with microscopic traffic simulations and connected vehicle field test data. Compared to queue length estimations without range sensors, the numerical experiments performed using microsimulations show that the model with range sensors improves the errors by 25% in variance-to-mean ratio and 5% in coefficient of variation at less than 20% market penetration rates. Using the connected vehicle field test data, it is observed that the fusing range sensor can improve close to 10 % without sensor estimators, 30 % of the queue back, and 48 % of the Highway Capacity Manual's control delay-based estimations at about 25 % market penetration rate. [ABSTRACT FROM AUTHOR]

The accuracy of stress estimates in Orthotropic Bridge Decks (OBD) may be negatively impacted by the complex load transfer and asphalt properties. Yet, accurate stress estimates are crucial for optimal fatigue verifications. A field measurement and modelling study has been conducted to evaluate the accuracy of finite element (FE) models. The level of detail of the FE model resembled engineering practice. Measurements comprised of strains caused by normal flowing traffic and by single vehicles with known load. The study gave insight into the distribution of the transverse vehicle location, dynamic vehicle–bridge deck interaction, and the asphalt influence. Comparing with international guidelines, the prediction error of FE models of OBDs is on the edge of acceptable. • Vehicle-induced strains are measured in an Orthotropic Bridge Deck (OBD). • Single vehicle tests and flowing traffic measurements are performed. • Data give insight in distribution of transverse location and asphalt influence. • Dynamic amplification appears a random factor with limited effect on fatigue. • Prediction errors of finite element models of OBDs are on the edge of acceptable. [ABSTRACT FROM AUTHOR]

Weigh-in-motion (WIM) systems provide the opportunity to measure the weight of vehicles, including individual axles, as they pass over the pavement. This study focuses on the evaluation of different WIM systems, the applications of WIM sensors, and the comparison of existing WIM sensors, particularly smart concrete sensors with self-sensing capabilities. To achieve this objective, various types of concrete sensors were investigated. These sensors are capable of measuring weight while in motion, monitoring the structural health of pavements, and collecting traffic data. Smart concrete sensors with self-sensing capabilities are composed of concrete composites that contain conductive or semi-conductive fillers, enabling the measurement of stress and strain through piezoresistive properties. The findings indicate that self-sensing concrete sensors can be effectively utilized for traffic properties measurement; however, ensuring their reliability in real-field conditions and practical applications necessitates further research. [ABSTRACT FROM AUTHOR]

• A systematic review of recently published articles on traffic conflict measurements based on PRISMA. • Validation of SSMs is needed in heterogeneous traffic environment, especially in the context of developing countries. • Importance of SSMs in new technologies like driver warning systems in driverless cars. • SSMs are not adequately capturing the likelihood and severity of accidents. • Study on the driver behavior at the unsignalized intersections. In recent times, the assessment of unsignalized intersection safety has received significant research attention because of the complex and diverse traffic movements and driving behaviour at such locations. However, priority traffic regulations are not well followed in comparison to the unsignalized junctions, which leads to more conflicts. Additionally, the severity of conflicts increases with continuous traffic manoeuvres, including right-turns and through traffic, combined with different driving behaviours. Several studies have compared crash-based analysis to proactive traffic safety measures. Current research outcomes imply that surrogate safety measures (SSMs) have the potential to elucidate the sequence of events that result in collisions, their underlying causes, and their outcomes. Therefore, to further understand the appropriateness of SSMs, further study is required based on heterogeneity in traffic along with driver behaviour that incorporates turning vehicle factors. This study presents an all-inclusive evaluation of the recent advancements in SSMs and their practical implementation, with a particular emphasis on unsignalized intersections in developing nations. The findings of this investigation would be helpful in identifying the appropriate safety indicators for evaluating traffic safety at unsignalized intersections. [ABSTRACT FROM AUTHOR]

Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart cities. Travelers as well as urban managers rely on reliable traffic information to make their decisions for route choice and traffic management. However, noisy or missing traffic data poses a problem for accurate and robust traffic forecasting. While data-driven models such as deep neural networks can achieve high prediction accuracy with complete datasets, sensor malfunctions, and environmental effects degrade the performance of such models, as these models rely heavily on precise traffic measurements for model training and estimation. Consequently, incomplete traffic data poses a challenge for robust model design that can make accurate traffic forecasts with noisy/missing data. This research proposes the Robust Spatiotemporal Graph Convolutional Network (RT-GCN), a traffic prediction model that handles noise perturbations and missing data using a Gaussian distributed node representation and a variance based attention mechanism. Through experiments conducted on four real-world traffic datasets using diverse noisy and missing scenarios, the proposed RT-GCN model has demonstrated its ability to handle noise perturbations and missing values and provide high accuracy prediction. • Gaussian distribution representation enhance inherent robustness of neural network. • Variance-based attention mechanism reduce the propagation of perturbation. • Batch Random Noise help improve robustness during training phase. • Models are tested on both noisy and missing datasets. • Diverse perturbation scenarios are considered in experiments. [ABSTRACT FROM AUTHOR]

• We investigate the impacts of HSR entry on air cargo traffic in China. • We quantify the distance level impacts of HSR entry on air belly-hold cargo traffic. • We utilize Chinese air cargo traffic data from January 2011 to December 2017. • The impacts of HSR on belly-hold cargo traffic are stronger among medium-haul flights Freighter cargo volumes and frequencies increase after the entry of HSR. Air cargo is vital to the modern supply chain because it provides efficient and timely delivery. In the past decade, the development of high-speed rail (HSR) in China has resulted in significant impacts on air cargo traffic, which has long been neglected by the existing studies. In this research, we quantify such impacts using HSR entry dummy variables and employ a panel regression approach to capture the impacts, specifically, the impacts on air cargo volumes and flight frequencies. We conduct a case study using quarterly air cargo traffic data in China, which contains 321 city pairs and spans between January 2011 and December 2017. We separately analyze the impacts on belly-hold cargo (carried by combi or passenger aircraft) traffic and freighter cargo (carried by freighters) traffic. We first capture the general impacts on belly-hold cargo traffic and freighter cargo traffic for all city pairs. Then, to quantify the distance level impacts on belly-hold cargo traffic, we categorize city pairs into multiple groups according to their distance and apply the panel regression approach in each group. Our main results include: (1) The entry of HSR services reduces the belly-hold cargo volumes and flight frequencies by 21.9% and 19.4%, respectively; (2) The strongest impacts on belly-hold cargo volumes and flight frequencies are observed in the medium-haul group within a distance of 800–1300 km; (3) After the entry of HSR services, the cargo volumes and flight frequencies of freighter cargo generally increase by 22.3% and 20.9%, respectively; (4) The whole air cargo traffic in the measurement of cargo volumes and flight frequencies decreases after the entry of HSR, and the impacts on whole air cargo volumes are relatively smaller than the belly-hold cargo volumes in the short-haul (no more than 800 km) and long-haul (more than 1300 km) groups. [ABSTRACT FROM AUTHOR]

Loop detector is the most common source of traffic flow measurements on freeways. Though being a mature technology, loop detector is prone to miscounts even when it is properly deployed and adjusted. If a loop detectors systematically over- or under-counts, the estimation based on vehicle conservation leads to results that are not physically plausible such as negative density or density higher than the jam-density. Here we propose an optimization-based method in which plausible values of accumulation is guaranteed through explicit constraints. Compared to similar methods, the proposed method distinct in the following: (i) the optimization problem is convex therefore leads to shorter computational time; (ii) occupancy is also taken into account in order to estimate upper and lower bounds in the accumulation between stations; (iii) no modifications are required when data from one or multiple lanes are missing. The method is first validated using micro-simulation by disturbing the loop-detector from micro-simulation, applying the method and then comparing the results with the ground truth data. The results show the method is able to capture the average counts almost exactly while providing estimate of density with satisfactory accuracy. The method is also successfully applied in a scenario in which field-data is available. [ABSTRACT FROM AUTHOR]

Dynamap (DYNamic Acoustic MAPping) is an European Life project whose main scope is to implement "real-time" acoustic maps in a district of the city of Milan. Dynamap is based on the idea of finding a suitable sets of roads displaying similar traffic noise behavior so that one can group them together into single noise maps. Twenty-four monitoring sensors have been installed permanently in appropriate locations within the pilot zone (District 9) to obtain six dynamic noise maps. The project has come to its "Test and Fault Analysis" phase. A set of new traffic noise and traffic flow measurements have been recorded to shed light on the reliability of Dynamap prediction. Besides the expected error due to statistical fluctuations of traffic noise, our preliminary analysis show a systematic error inherent to the adopted traffic flow model, which is an essential ingredient of the whole approach. By correlating the traffic noise prediction error to the one due to the traffic flow model, we succeed in improving the Dynamap prediction thus limiting the overall error to within about 3 dB. We suggest that Dynamap scheme can be applied to large urban conglomerates in general, thus becoming a useful tool for monitoring traffic noise accurately and efficiently in large cities. [ABSTRACT FROM AUTHOR]

• A new method to measure traffic-induced settlement at different layers of embankment is presented. • The measurement equipment is installed as the embankment height increased. • An in-situ comparison experiment was carried out to verify the stability of the equipment. A large number of high embankments have been constructed in highway engineering with the fast development of highway. The measurement and treatment of traffic loading-induced settlement is a research focus in recent years. This paper focuses on the measurement of the layered settlement, induced by the traffic loading, in the embankment of highway. A novel method to measure the traffic loading-induced settlement at different depths in the embankment is proposed. The practical measurement equipment, including a multipoint extensometer, rigid base, settlement detecting plate, protector tube and connecting rod, is developed. The field tests were carried out in the filling and surcharge period to verify the stability and accuracy of measurement equipment. The test results indicate that the developed equipment and the presented installation method can be capable of measuring traffic-induced settlement at different depths in the embankment of the operating highway. [ABSTRACT FROM AUTHOR]

Accurately estimating annual average daily traffic (AADT) on minor roads is essential for assessing traffic-related air pollution (TRAP) exposure, particularly in areas where most people live. Our study assessed the direct and indirect external validity of three methods used to estimate AADT on minor roads in Melbourne, Australia. We estimated the minor road AADT using a fixed-value approach (assuming 600 vehicles/day) and linear and negative binomial (NB) models. The models were generated using road type, road importance index, AADT and distance of the nearest major road, population density, workplace density, and weighted road density. External measurements of traffic counts, as well as black carbon (BC) and ultrafine particles (UFP), were conducted at 201 sites for direct and indirect validation, respectively. Statistical tests included Akaike information criterion (AIC) to compare models' performance, the concordance correlation coefficient (CCC) for direct validation, and Spearman's correlation coefficient for indirect validation. Results show that 88.5% of the roads in Melbourne are minor, yet only 18.9% have AADT. The performance assessment of minor road models indicated comparable performance for both models (AIC of 1,023,686 vs. 1,058,502). In the direct validation with external traffic measurements, there was no difference between the three methods for overall minor roads. However, for minor roads within residential areas, CCC (95% confidence interval [CI]) values were −0.001 (−0.17; 0.18), 0.47 (0.32; 0.60), and 0.29 (0.18; 0.39) for the fixed-value approach, the linear model, and the NB model, respectively. In the indirect validation, we found differences only on UFP where the Spearman's correlation (95% CI) for both models and fixed-value approach were 0.50 (0.37; 0.62) and 0.34 (0.19; 0.48), respectively. In conclusion, our linear model outperformed the fixed-value approach when compared against traffic and TRAP measurements. The methodology followed in this study is relevant to locations with incomplete minor road AADT data. [Display omitted] • Minor roads contribute to air pollution but their traffic volume is largely unknown. • Direct external validation of models showed good performance in residential areas. • Indirect external validation of ultrafine particle models showed good performance. • The models can estimate minor roads' traffic volume in large cities. • The method is relevant to locations with incomplete minor road traffic volume data. [ABSTRACT FROM AUTHOR]

The passive capture of Wi-Fi traces using sniffers is a promising technique for characterizing the wireless activity of a target area without disturbing users with intrusive measurement tools. The main problem with this technique is that individual sniffers miss packets, which may lead to an inconsistent representation of the scenario. In this paper, we advocate that redundancy (i.e., collocating two or more individual sniffers) is necessary to achieve a reasonable picture of the wireless traffic at the time of the measurement. We formulate the notion of traffic completeness and investigate it experimentally by analyzing Wi-Fi traces obtained by up to 14 co-located sniffers (Raspberry Pi 4) in an office area. We make several observations, including the fact that all individual sniffers capture packets that none of the other sniffers capture. Our results confirm that, depending on the level of completeness that experimentation needs, redundancy is necessary. Moreover, we highlight the importance of characterization of the wireless environment. We define and analyze a few metrics for the characterization. [ABSTRACT FROM AUTHOR]

With the increasing demand for chemical products in China, how to manage the risks associated with hazardous freight transportation has attracted arising attentions from both government and industry practitioners. From the perspective of carrier companies, transportation modal choice and routing for hazardous freight transportation need to be carefully designed, which motivates current research. This paper aims to optimize the service mode and routing for hazardous freight transportation in a rail-truck network with bi-objectives optimization approach. Several practical concerns are addressed, including the coordination of rail transportation which has fixed train running plan and road transportation which is flexible and mobile, and the consideration of alternative path when the expressway is temporally blocked for hazardous freight due to the traffic restriction measurements on inter-city road segments. Furthermore, the time-varying transportation risks and costs associated with accidents of hazardous freight transportation are investigated. The Max-Min Ant-colony Algorithm (MMAS) algorithm is adopted for solution. For model validations and conclusion generalization, this paper implements a real-life case study in Beijing-Tianjin-Hebei region in China with the objective of minimizing transportation risks and costs. The solutions show that traffic restrictions have an adverse impact on the carrier and alternative paths are able to help the carriers further optimize their routing strategy. A careful consideration and appropriate processing of differences in operational characteristics of two transportation modes, is able to provide a better and more realistic understanding of rail-truck hazardous freight transportation problem. [ABSTRACT FROM AUTHOR]

• Injury severities of expressway crashes in 3 types of tunnels were estimated. • Two unobserved heterogeneity models were adopted to obtain robust results. • Certain differences exited in the contributing factors in the 3 types of tunnels. • Likelihood ratio tests and out-of-sample predictions were investigated. • Several targeted countermeasures were provided for eliminating injury severities. This study examines the non-transferability of the determinants affecting injury severities of tunnels of three grades classified by different lengths. Based on tunnel crash data in Shanxi Province during (2012–2018), three possible crash injury severity categories (no injury, minor injury, and severe injury) are estimated. Two alternate models were used to account for unobserved heterogeneity: a random parameters multinomial logit (RPML) model with heterogeneity in means and variances and a latent-class multinomial logit (LCML) model with class probability functions. Non-transferability in the effects of explanatory variables is confirmed using two series of likelihood ratio tests based on the two alternate models. A wide variety of variables is observed to determine pedestrian-injury severities. The estimation results show significant non-transferability across the three grades in both RPML and LCML models. In addition, several explanatory variables produce relatively transferable effects, which can provide some valuable insights to implement effective countermeasures. Moreover, out-of-sample predictions are simulated to confirm the non-transferability, while the LC models produce higher differences for the non-transferability than RP models. According to estimation results, several practical measurements concerning education programs, traffic enforcement, emergency response systems, and traffic safety measurements are implemented to eliminate tunnel crash outcomes. Understanding and depth comparing the estimation results, likelihood ratio tests and out-of-sample predictions using alternate models is a promising direction for future research to explore how the observed and unobserved heterogeneity can be estimated. [ABSTRACT FROM AUTHOR]

To optimise surgical outcomes and minimise the risk of infections, effective communication and teamwork are essential in the operating theatre (OT). In this pilot study, we mapped the OT team dynamics and roles in infection and safety-related practices. Between November 2021 and February 2022, data were gathered from adult surgical teams at a tertiary hospital in India. Data included direct ethnographic observations of OT activities and interactions, sociograms of targeted infection and safety-related communication, measurement of OT traffic, and focus group discussions with teams. Qualitative data were coded and analysed using a grounded theory approach. Quantitative data were analysed using descriptive statistics. Data gathered from 51 hours of direct observations across ten surgical pathways include 16 sociograms and 15 traffic-flow maps. Team hierarchies and dynamics are directly influenced by the senior surgeon. The OT staff adhere to implicitly understood roles, creating a missed opportunity for infection-related interventions. Instances of non-verbal communication are common, especially for routine activities. While the surgical and anaesthetic residents and technicians lead most tasks during procedures, the scrub nurse acts as a mediator coordinating and activating tasks among role players across hierarchies. Despite this role, the plan of events is not always communicated to them ahead of the procedure resulting in multiple door openings to source equipment and disposables. Traffic flow, counted by the number of door openings reach up to 14-15 per five minutes, with implications for infection management. Communication around the WHO checklist was limited. While interventions exist to optimise care in the OT, implementation challenges remain. Assigning explicit roles and responsibilities for actioning, including the WHO checklist completion, to specific team members is critical. On reflexive feedback to teams, the visual methods utilised provided an effective means for team reflection and an awareness of practices that may compromise infection management and patient safety. [ABSTRACT FROM AUTHOR]

Crowd-sourced traffic data potentially allow prediction of traffic-related air pollution at high temporal and spatial resolution. To examine associations (1) of radar-based traffic measurements with congestion colors displayed on crowd-sourced traffic data maps and (2) of black carbon (BC) levels with radar and crowd-sourced traffic data. At an off-ramp of an interstate and a small one-way street in a mixed-use area in New York City, we used radar devices to obtain vehicle speeds and flows (hourly counts) for cars and trucks. At these radar sites and at an additional non-radar equipped site at a 2-way street, we monitored BC levels using aethalometers in the summer and early fall of 2017. At all three sites, free-flow traffic conditions typically did not occur due to the nearby presence of traffic lights and forced turns. We also downloaded real-time traffic maps from a crowd-sourced traffic data provider and assigned an ordinal integer congestion color code CCC to the congestion colors, ranging from 1 (dark red) to 5 (gray). CCC increased with vehicle speed. Traffic flow was highest for intermediate speeds and intermediate CCC. Regression analyses showed that BC levels increased with either segregated or total vehicle flows. At the off-ramp, time-dependent BC levels can be inferred from time-dependent CCC and radar-derived mean vehicle flow data. A unit decrease in CCC for a mean traffic flow of 100 vehicles/h was associated with a mean (95% CI) increase in BC levels of 0.023 (0.028, 0.018) μg/m3. At the small 1-way and the 2-way street, BC levels were also negatively associated with CCC, though at a >0.05 significance level. Use of inexpensive crowd-sourced traffic data holds great promise in air pollution modeling and health studies. Time-dependent traffic-related primary air pollution levels may be inferred from radar-calibrated crowd-sourced traffic data, in our case radar-derived mean traffic flow and widely available CCC data. However, at some locations mean traffic flow data may already be available. • Crowd-sourced traffic data can be calibrated by traffic-radar measurements • Traffic-related black carbon levels depend on traffic volume but may also depend on vehicle speed • Traffic-related black carbon levels can be associated with congestion colors displayed on crowd-sourced traffic maps • Use of inexpensive crowd-sourced traffic data holds great promise in air pollution modeling and health studies [ABSTRACT FROM AUTHOR]

Advanced analysis of road traffic data is an essential component of today's intelligent transportation systems. This paper presents a video-based vehicle speed measurement system based on a proposed mathematical model using a movement pattern vector as an input variable. The system uses the intrusion line technique to measure the movement pattern vector with low computational complexity. Further, the mathematical model introduced to generate the pdf (probability density function) of a vehicle's speed that improves the speed estimate. As a result, the presented model provides a reliable framework with which to optically measure the speeds of passing vehicles with high accuracy. As a proof of concept, the proposed method was tested on a busy highway under realistic circumstances. The results were validated by a GPS (Global Positioning System)-equipped car and the traffic regulations at the measurement site. The experimental results are promising, with an average error of 1.77 % in challenging scenarios. [ABSTRACT FROM AUTHOR]

Loop detector is the most common source of traffic flow measurements on freeways. Though being a mature technology, loop detector is prone to miscounts even when it is properly deployed and adjusted. If a loop detectors systematically over- or under-counts, the estimation based on vehicle conservation leads to results that are not physically plausible such as negative density or density higher than the jam-density. Here we propose an optimization-based method in which plausible values of accumulation is guaranteed through explicit constraints. Compared to similar methods, the proposed method distinct in the following: (i) the optimization problem is convex therefore leads to shorter computational time; (ii) occupancy is also taken into account in order to estimate upper and lower bounds in the accumulation between stations; (iii) no modifications are required when data from one or multiple lanes are missing. The method is first validated using micro-simulation by disturbing the loop-detector from micro-simulation, applying the method and then comparing the results with the ground truth data. The results show the method is able to capture the average counts almost exactly while providing estimate of density with satisfactory accuracy. The method is also successfully applied in a scenario in which field-data is available. [ABSTRACT FROM AUTHOR]

The dynamics of data traffic intensity is examined using traffic measurements at the interface switch input. The wish to prevent failures of trunk line equipment and take the full advantage of network resources makes it necessary to be able to predict the network usage. The research tackles the problem of building a predicting neural-net model of the time sequence of network traffic. Topological data analysis methods are used for data preprocessing. Nonlinear dynamics algorithms are used to choose the neural net architecture. Topological data analysis methods allow the computation of time sequence invariants. The probability function for random field maxima cannot be described analytically. However, computational topology algorithms make it possible to approximate this function using the expected value of Euler's characteristic defined over a set of peaks. The expected values of Euler's characteristic are found by constructing persistence diagrams and computing barcode lengths. A solution of the problem with the help of R-based libraries is given. The computation of Euler's characteristics allows us to divide the whole data set into several uniform subsets. Predicting neural-net models are built for each of such subsets. Whitney and Takens theorems are used for determining the architecture of the sought-for neural net model. According to these theorems, the associative properties of a mathematical model depend on how accurate the dimensionality of the dynamic system is defined. The sub-problem is solved using nonlinear dynamics algorithms and calculating the correlation integral. The goal of the research is to provide ways to secure the effective transmission of data packets. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A methodology to assess road construction sensitive to road slope for energyor GWP. • A methodology to assess road operations sensitive to road slope for energy and GWP. • An optimization of road construction and operation with respect to the road slope. • An application on a real case which proves the relevance of the methodology. Abstract This paper proposes a methodology to optimize the longitudinal profile of roads according to either an energy consumption or Global Warming Potential (GWP) criterion calculated for both construction and operation phases. For the construction phase assessment, this methodology is based on a earthworks model that computes the geometric differences between the natural terrain and the longitudinal road profile and moreover uses environmental data validated with real experiments. The operation phase is assessed by simulating traffic over a ten-years period. Traffic simulations are based on vehicle dynamic models, also validated with real experiments. The optimization problem is set up in a finite dimensional optimization. A case study illustrates this methodology. By taking into account actual traffic measurements, the optimized profile decreases by 6% the total primary energy consumption and by 8% the GWP. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to evaluate the real-time crash risk of freeways by using real-world traffic flow data. The crash risk expressed as the potential crash likelihood is assessed under variable speed limit (VSL) and without VSL, in which both spatial correlation between different sites and temporal similarity are contained. Traffic flow data of Whitemud Drive network (WMD) in Canada is used to perform the relevant analysis, including VSL implementation analysis, traffic flow similarity analysis, crash risk and congestion analysis. Analytical results demonstrate that the average traffic flow under VSL schemes 1, 2, 3 and 4 are highly correlated from spatial-temporal perspective. The crash likelihoods and congestions under these VSL schemes are greatly improved. The best VSL control scheme, the most dangerous area and time, together with the most congested station of WMD are eventually determined. Subsequently, a t -test is employed to examine the significance of these results. t -Test results suggest that the improvement degree between crash risk and congestion under the best VSL control scheme show a difference, i.e., the best VSL control scheme can reduce the crash risk of moderate risk area more than high risk area, while it may have a larger melioration on the most congested area than the relatively uncongested area. Finally, these results are considered to have the potential reference in the mitigation of WMD traffic issues. [ABSTRACT FROM AUTHOR]

A robust signal control system (AROSS) for equilibrium flow is presented for urban road networks under uncertain travel demand and traffic delay. The optimal solutions for AROSS can be formulated as a smoothing mathematical program with equilibrium constraints (SMPEC) and efficiently solved by a projected bundle set method with global convergence. While conventional robust optimization is considered a good alternative against high-consequence realization of uncertainty, a traffic responsive signal control using a hybrid strategy of scenarios is considered. Sensitivity analysis of SMPEC with respect to signal settings against randomized budget uncertainty in travel demand and link delay is performed. Numerical comparisons are also made with other heuristics using real-data road networks. Results indicate that AROSS enjoyed a greater gain of achieving road network robustness while effectively attenuating sub-optimality of robust optimum as compared to recent alternatives. [ABSTRACT FROM AUTHOR]

Ramp Metering (RM) is one of the most effective control techniques to alleviate freeway congestion. We consider RM at the microscopic level subject to vehicle following safety constraints for a freeway with arbitrary number of on- and off-ramps. The arrival times of vehicles to the on-ramps, as well as their destinations are modeled by exogenous stochastic processes. Once a vehicle is released from an on-ramp, it accelerates towards the free flow speed if it is not obstructed by another vehicle; once it gets close to another vehicle, it adopts a safe gap vehicle following behavior. The vehicle exits the freeway once it reaches its destination off-ramp. We design traffic-responsive RM policies that maximize the throughput. For a given routing matrix, the throughput of a RM policy is characterized by the set of on-ramp arrival rates for which the queue sizes at all the on-ramps remain bounded in expectation. The proposed RM policies operate under vehicle following safety constraints, where new vehicles are released only if there is sufficient gap between vehicles on the mainline at the moment of release. Furthermore, the proposed policies work in synchronous cycles during which an on-ramp does not release more vehicles than the number of vehicles waiting in its queue at the start of the cycle. All the proposed policies are reactive , meaning that they only require real-time traffic measurements without the need for demand prediction. However, they differ in how they use the traffic measurements. In particular, we provide three different mechanisms under which each on-ramp either: (i) pauses release for a time interval before the next cycle begins, or (ii) adjusts the release rate during a cycle, or (iii) adopts a conservative safe gap criterion for release during a cycle. The throughput of the proposed policies is characterized by studying stochastic stability of the induced Markov chains, and is proven to be maximized when the merging speed at all the on-ramps equals the free flow speed. Simulations are provided to illustrate the performance of our policies and compare with a well-known RM policy from the literature which relies on local traffic measurements. • Designing traffic-responsive Ramp Metering (RM) policies at the microscopic level. • Analyzing the interplay of safety, connectivity, automation protocols, and RM throughput. • Designing RM policies that reduce the risk of collision at merging bottlenecks without compromising the throughput. [ABSTRACT FROM AUTHOR]

Estimates of tyre and brake wear emission factors are presented, derived from data collected from roadside and urban background sites on the premises of the University of Birmingham, located in the UK's second largest city. Size-fractionated particulate matter samples were collected at both sites concurrently in the spring/summer of 2019 and analysed for elemental concentrations and magnetic properties. Using Positive Matrix Factorisation (PMF), three sources were identified in the roadside mass increment of the 1.0–9.9 μm stages of MOUDI impactors located at both sites, namely: brake dust (7.1%); tyre dust (9.6%); and crustal (83%). The large fraction of the mass apportioned to crustal material was suspected to be mainly from a nearby construction site rather than resuspension of road dust. By using Ba and Zn as elemental tracers, brake and tyre wear emission factors were estimated as 7.4 mg/veh.km and 9.9 mg/veh.km, respectively, compared with the PMF-derived equivalent values of 4.4 mg/veh.km and 11 mg/veh.km. Based on the magnetic measurements, an emission factor can be estimated independently for brake dust of 4.7 mg/veh.km. A further analysis was carried out on the concurrently measured roadside increment in the particle number size distribution (10 nm-10 μm). Four factors were identified in the hourly measurements: traffic exhaust nucleation; traffic exhaust solid particles; windblown dust; and an unknown source. The high increment of the windblown dust factor, 3.2 μg/m3, was comparable in magnitude to the crustal factor measured using the MOUDI samples (3.5 μg/m3). The latter's polar plot indicated that this factor was dominated by a large neighbouring construction site. The number emission factors of the exhaust solid particle and exhaust nucleation factors were estimated as 2.8 and 1.9 x 1012/veh.km, respectively. [Display omitted] • Simultaneous collection of size-fractionated particle samples at roadside and background. • Particle size distributions (10 nm - 10 μm) measured simultaneously at both sites. • Elemental tracers and PMF used to estimate emission factors for brake and tyre wear. • Particle magnetic properties used to estimate an emission factor for brake wear. • Good agreement between emission factors estimated by different methods. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic Driver-Pressure-State-Impact-Response (DPSIR) method for traffic performance assessment under urban floods caused by extreme rainfall events. A coupled rainfall-flood-traffic model has been developed as the prerequisite for evaluation with publicly available data. The DPSIR method integrates the natural and transportation systems into cause-effect-response impact chains and facilitates dynamic traffic performance measurement on the road and network levels. We apply this approach to the urban networks' motorways, trunk, and arterial roads in Shanghai, China. The results help identify critical roads and verify the effectiveness of demand-control strategies in improving traffic resilience. By offering decision-makers a comprehensive view of traffic operation under disruptions, this method can help them build a more resilient transport system in flood risk management. [ABSTRACT FROM AUTHOR]

Ultrafine particles (UFP, <100 nm) often dominate the aerosol number concentration in urban environments. The resulting high particle concentrations can be as high as 106 particles cm−3. Such high concentrations are a challenge for the instruments, as the measurements based on condensation particle counters, are typically designed to be sensitive for lower concentrations. Therefore, a diluter is often employed to lower the concentrations down to the measurement range of the instrument. However, diluters are often unsuitable for measuring cluster or nucleation mode particles due to high losses. Here, we present the new Airmodus nanoparticle diluter (AND) sampling system and characterize its performance at different dilution factors over a wide particle size range down to ∼1 nm size. In addition to diluting the sample, AND can be used as an ion precipitator to remove small ions from the sampled air or to lower the relative humidity of the sampled air. The first results from the ambient traffic emission measurements are also shown. • New Airmodus nanoparticle diluter (AND) sampling system is presented. • Performance at different dilution factors is presented over a wide particle size range down to ∼1 nm. • Instrument ion precipitator is observed to scavenge ions effectively at the diameters below 8 nm. • Instrument performance was tested under ambient conditions with emissions from traffic. [ABSTRACT FROM AUTHOR]

• Analyzed dynamic loadingimpact on traffic speed deflectometer (TSD) measurement. • Developed semi-analytical finite element model for dynamic loading on pavement. • Studied the relationship between dynamic loads and deflection measurements. • Quantified effect of pavement roughness on measurement error using AUTC method. Traffic speed deflectometer (TSD) provides an efficient way for evaluating pavement condition. The current principle is to convert measured deflection slopes into the deflection basin using the Area Under The Curve (AUTC) method based on the time-space transform and elastic material theory. This study aims to investigate impact of dynamic loading due to pavement roughness on TSD measurements. Semi-analytical finite element (SAFEM) method was developed to simulate dynamic moving loads on flexible pavements with viscoelastic asphalt layer. The variations of pavement deflection and deflection velocity were found increasing linearly with dynamic loading coefficient (DLC), especially for deflection velocity. The average relative error between the actual deflection basin and the one from the AUTC method could be 15% at DLC of 0.1 with the highest error for the maximum deflection. This indicates the necessity of developing correction method of TSD deflection measurements on rough pavements. [ABSTRACT FROM AUTHOR]

This study investigates the system-wide traffic flow re-allocation effect of speed limits in uncertain environments. Previous studies have only considered link capacity degradation, which is only one of the factors that lead to supply uncertainty. This study examines how imposing speed limits reallocates the traffic flows in a situation of general supply uncertainty with risk-averse travelers. The effects of imposing a link-specific speed limit on link driving speed and travel time are analyzed, given the link travel time distribution before imposing the speed limit. The expected travel time and travel time standard deviation of a link with a speed limit are derived from the link travel time distribution and are both continuous, monotone, and convex functions in terms of link flow. A distribution-free, reliability-based user equilibrium with speed limits is established, in which travelers are assumed to choose routes that minimize their own travel time budget. A variational inequality formulation for the equilibrium problem is proposed and the solution properties are provided. In this study, the inefficiency of a reliability-based user equilibrium flow pattern with speed limits is defined and found to be bounded above when supply uncertainty refers to capacity degradation. The upper bound depends on the level of risk aversion of travelers, a ratio related to the design and worst-case link capacities, and the highest power of all link performance functions. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive background extraction method in traffic scenes. Based on running average method, the proposed method uses the hierarchical learning factor which has different values in different levels to extract background. Traditionally, the running average method uses a fixed learning factor. It has some shortcomings: if the value of learning factor taken is too large, ghosts are prone to occur in the background learning process; if the value is too small, the speed of background extraction will be slow. In the proposed method, the value of image texture entropy is used to measure the convergence degree of background. According to the convergence degree, the learning factors are selected in an adaptive manner. Experiments prove that the proposed method can overcome the above shortcomings of traditional running average method and can faster extract the background without ghosts. The background composition curve analysis shows that this method is reasonable due to reasonable component distribution without excessive reliance on certain video frames. In addition, the proposed method is compared with some state-of-the-art methods. The comparison shows that our method has high detection accuracy and computation speed. [ABSTRACT FROM AUTHOR]

In this paper, we present necessary premises for the deployment of the Internet of Vehicles (IoV) integrating Big Data analytics of road network traffic measurements of the city of Mohammedia, Morocco. Thus, we introduce an architecture based on three main layers such as IoV, Fog Computing and Cloud Computing Layer. We specifically put more focus on Fog Computing layer in which we develop a framework for a real-time collecting and processing events generated by intelligent vehicles as well as visualizing traffic state on each road section. Furthermore, we consider deployment and test of the proposed framework using events retrieved from a Vanets -type micro simulation. Finally, we present and discuss the first obtained results as well as the advantages and limitations of the proposed architecture. [ABSTRACT FROM AUTHOR]

This paper is part of an ongoing research that analyzes the feasibility of implementing “buyout” auctions for metering traffic inflow at special use lanes (such as high-occupancy/toll lanes). The term “buyout” refers to the fact that these games allow skipping the auction by paying a price. The ongoing research suggests that high consideration needs to be put on the road design before developing a game-theoretic model. It also suggests that from an operations point of view, if a “buyout lottery” system can be implemented successfully, then its conversion to a buyout auction system becomes straightforward. Therefore, this paper aims at: (1) testing a buyout lottery-based metering (LBM) system in a real case scenario, and (2) introducing a game-theoretic model that recommends to its users the strategy to adopt when deciding between paying the toll or playing the lottery. This paper introduces a microsimulation model of an LBM system that is applied to the city of Minneapolis. This model is then used to evaluate the system’s congestion and revenue effects, always assuming that drivers adopt what is referred as the “safest strategy”. In the evaluation, a set of most likely lottery scenarios is also obtained. Finally, a game-theoretic model is introduced for obtaining the best strategy. This strategy is compared with the safest strategy, in the context of the most likely lottery scenarios. The results from the microsimulation model suggest that the LBM system reduces congestion significantly and increases revenue. The best strategy obtained from the game-theoretic model presents challenges for its calculation. But in the context of the most likely scenarios, this paper suggests that drivers should adopt the safest strategy. The safest strategy may coincide with what are commonly known as “minimax regret” and “maximin” strategies. [ABSTRACT FROM AUTHOR]

In this paper, a reliable cellular automata model oriented to faithfully reproduce deceleration and acceleration according to realistic reactions of drivers, when vehicles with different deceleration capabilities are considered is presented. The model focuses on describing complex traffic phenomena by coding in its rules the basic mechanisms of drivers behavior, vehicles capabilities and kinetics, while preserving simplicity. In particular, vehiclés kinetics is based on uniform accelerated motion, rather than in impulsive accelerated motion as in most existing CA models. Thus, the proposed model calculates in an analytic way three safe preserving distances to determine the best action a follower vehicle can take under a worst case scenario. Besides, the prediction analysis guarantees that under the proper assumptions, collision between vehicles may not happen at any future time. Simulations results indicate that all interactions of heterogeneous vehicles (i.e., car–truck, truck–car, car–car and truck–truck) are properly reproduced by the model. In addition, the model overcomes one of the major limitations of CA models for traffic modeling: the inability to perform smooth approach to slower or stopped vehicles. Moreover, the model is also capable of reproducing most empirical findings including the backward speed of the downstream front of the traffic jam, and different congested traffic patterns induced by a system with open boundary conditions with an on-ramp. Like most CA models, integer values are used to make the model run faster, which makes the proposed model suitable for real time traffic simulation of large networks. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a heterogeneous traffic-flow model to study the possible impact of connected and autonomous vehicles (CAVs) on the traffic flow. Based on a recently proposed two-state safe-speed model (TSM), a two-lane cellular automaton (CA) model was developed, wherein both the CAVs and conventional vehicles were incorporated in the heterogeneous traffic flow. In particular, operation rules for CAVs are established considering the new characteristics of this emerging technology, including autonomous driving through the adaptive cruise control and inter-vehicle connection via short-range communication. Simulations were conducted under various CAV-penetration rates in the heterogeneous flow. The impact of CAVs on the road capacity was numerically investigated. The simulation results indicate that the road capacity increases with an increase in the CAV-penetration rate within the heterogeneous flow. Up to a CAV-penetration rate of 30%, the road capacity increases gradually; the effect of the difference in the CAV capability on the growth rate is insignificant. When the CAV-penetration rate exceeds 30%, the growth rate is largely decided by the capability of the CAV. The greater the capability, the higher the road-capacity growth rate. The relationship between the CAV-penetration rate and the road capacity is numerically analyzed, providing some insights into the possible impact of the CAVs on traffic systems. [ABSTRACT FROM AUTHOR]

This paper studies the traffic flow in a grid road network with non-signalized intersections. The nature of the drivers in the network is simulated such that they play an iterative snowdrift game with other drivers. A cellular automata model is applied to study the characteristics of the traffic flow and the evolution of the behaviour of the drivers during the game. The drivers use best-response as their strategy to update rules. Three major findings are revealed. First, the cooperation rate in simulation experiences staircase-shaped drop as cost to benefit ratio r increases, and cooperation rate can be derived analytically as a function of cost to benefit ratio r . Second, we find that higher cooperation rate corresponds to higher average speed, lower density and higher flow. This reveals that defectors deteriorate the efficiency of traffic on non-signalized intersections. Third, the system experiences more randomness when the density is low because the drivers will not have much opportunity to update strategy when the density is low. These findings help to show how the strategy of drivers in a traffic network evolves and how their interactions influence the overall performance of the traffic system. [ABSTRACT FROM AUTHOR]

Cellular signaling data provide a massive and emerging source for acquiring urban origin–destination (OD) travel flow information, supporting decision making on large-scale mobility enhancement, and enabling the exploration of factors that influence travel demand. This study investigates the effects of population, facilities, and transit accessibility on travel flows between traffic analysis zones. A spatial econometric model is employed for the OD travel flow analysis by integrating massive mobile data with other explanatory features of urban regions. The results of real-world applications in Hangzhou, China, show that: (I) all coefficients of the origin dependence, destination dependence, and OD dependence are statistically significant, which verifies the consideration of the spatial interdependence in the OD flow modeling; (II) all of the permanent population, number of facilities, and transit accessibility have positive correlations with travel flows; and (III) travel time, as expected, is negatively correlated with the travel flow volume. Finally, policy implications are discussed, further contributing to the design of urban land use and transportation policies. [ABSTRACT FROM AUTHOR]

Adding capacity is one policy mechanism to alleviate congestion. However, the empirical evidence strongly suggests that additional capacity only makes congestion worse. This study analyzes the differential effects of additional freeway capacity versus additional arterial capacity on vehicle kilometers of travel (VKT) in metropolitan areas across the U.S. The analysis uses vehicle data and household data from the 2001 and the 2009 National Household Travel Surveys (NHTS) and includes stock and flow measures of road capacity, road congestion, commuter demand, and economic growth for metropolitan areas. Taking into account differences between metropolitan areas on each measure, the study adopts a novel multilevel model approach to estimate how additional capacity affects VKT. Results indicate that adding more arterial capacity slightly decreases VKT over a lag period from six years (1995 to 2001) to eight years (2001 to 2009), probably because adding arterials shortens routes between origins and destinations more so than adding freeways. Consistent with expectations, VKT is lower in more congested metropolitan areas, and in metropolitan areas that got more congested. Results also indicate that rebound effects (higher fuel-economy vehicles are driven much more than lower fuel-economy vehicles) will at least partially offset the demand management benefits of (gasoline) price sensitivity (higher gasoline prices decrease VKT). [ABSTRACT FROM AUTHOR]

The implementation of a charging infrastructure network is the necessary prerequisite for the diffusion of Electric Vehicles (EVs). In this paper a methodology to calculate the required number of charging stations for EVs and to set their position in a road network is proposed. The aim is to planning the distribution of services area to host charging infrastructures. Using the demand (the flow of EVs) and the supply (the road network where they will be positioned) through a two-level model were the locations initially identified (first level) and thereafter the number of charging stations for each service area (second level) evaluated. The paper deals with the intersection of three main topics: the vehicle technologies (engine and battery pack specifications), the charging station characteristics and the EVs flow. After verifying the model and the solution procedure on a test road network, the methodology is applied in a high dimension case, considering the Italian highway network. [ABSTRACT FROM AUTHOR]

Technological limitations and practical difficulties cause inevitable losses of traffic data in the typical processing chain of an intelligent transportation system. This has motivated the development of imputation algorithms for mitigating the consequences of such losses. As the involved datasets are usually multidimensional and bear strong spatio-temporal correlations, we propose for traffic data imputation a tensor completion algorithm which promotes parsimony of an estimated orthogonal Tucker model by iteratively softly thresholding its core. The motivation of this strategy is discussed on the basis of characteristics typically possessed by real-world datasets. An evaluation of the proposed method using speed data from the Grenoble south ring (France) shows that our algorithm outperforms other imputation methods, including tensor completion algorithms, and delivers good results even when the loss is severely systematic, being mostly concentrated in long time windows (of up to three hours) spread along the considered time horizon. [ABSTRACT FROM AUTHOR]

Travel time is an important index for managers to evaluate the performance of transportation systems and an intuitive measure for travelers to choose routes and departure times. An important part of the literature focuses on predicting instantaneous travel time under recurrent traffic conditions to disseminate traffic information. However, accurate travel time prediction is important for assessing the effects of abnormal traffic conditions and helping travelers make reliable travel decisions under such conditions. This study proposes an online travel time prediction model with emphasis on capturing the effects of anomalies. The model divides a path into short links. A Functional Principal Component Analysis (FPCA) framework is adopted to forecast link travel times based on historical data and real-time measurements. Furthermore, a probabilistic nested delay operator is used to calculate path travel time distributions. To ensure that the algorithm is fast enough for online applications, parallel computation architecture is introduced to overcome the computational burden of the FPCA. Finally, a rolling horizon structure is applied to online travel time prediction. Empirical results for Guangzhou Airport Expressway indicate that the proposed method can capture an abrupt change in traffic state and provide a promising and reliable travel time prediction at both the link and path levels. In the case where the original FPCA is modified for parallelization, accuracy and computational effort are evaluated and compared with those of the sequential algorithm. The proposed algorithm is found to require only a piece rather than a large set of traffic incident records. [ABSTRACT FROM AUTHOR]

This paper presents the traffic volume estimation by constructing a statistical model using dynamic response features acquired in the structural health monitoring of an in-service cable-stayed bridge. The study started from the correlation coefficient analysis between some dynamic response features and the environmental and operational effects. It was found that the response amplitudes at several resonant modes were dominantly influenced by the traffic volume. The numerical analysis was thus conducted to understand the relationship between the traffic flow parameters; speed and density, and the bridge dynamic responses. The results showed that the sensitivity of acceleration amplitude to the traffic flow speed increased with increasing the traffic density. A linear regression model to estimate the traffic volume was then constructed using the training data. The explanatory variables were determined by a procedure that considered the correlation coefficients of possible features to the traffic volume and their multicollinearity. The constructed regression model showed the accurate fitting performance to the traffic data, and it was also capable of predicting both the traffic volume per five minute and the average daily traffic (ADT). [ABSTRACT FROM AUTHOR]

This paper presents an empirical study of the fundamental relationship between speed, v, and flow, q, (denoted vqFR) under low flow in the uncongested regime. Using new analytical techniques to extract more information from loop detector data, the vqFR from a time of day HOV lane exhibits high v that slowly drops as q increases. This curve arises after binning several million vehicles by q and only considering those bins with q < 1200 vph. A surprising thing happens when further binning the data by the adjacent lane speed (v2): the vqFR expands in to a fan of curves that decrease in magnitude and slope with decreasing v2. Yet each curve in the fan continues to exhibit uncongested trends, ranging from a flat curve consistent with recent editions of the Highway Capacity Manual to downward sloping curves. It is shown that this behavior was not due to the HOV operations per se, the same behavior also arises in the non-HOV period when the lane serves all vehicles and it is also observed at another facility without any HOV restrictions. This dependency on the adjacent lane is absent from most traffic flow theories. Taking a broader view, four different factors appear to limit the speed a driver takes: (i) the roadway geometry, (ii) the posted speed limit, (iii) the vehicle ahead (car following), and (iv) traffic conditions in the adjacent lane. Whichever constraint is most binding determines the driver's speed. While the first three constraints are found in the literature, this work contributes the fourth, as per above. When the speed limit is the most binding constraint the uncongested regime of the vqFR is roughly flat with a near constant speed over a wide range of q. When the roadway geometry is the binding constraint, e.g., due to the lack of speed limits, drivers are able to travel fast enough to be sensitive to the vehicle ahead and exhibit lower v as q increases. Car following is by definition in the congested regime and thus, beyond the scope of this paper. Finally, the present work shows that as the adjacent lane moves slower, the uncongested drivers choose speeds below the speed limit and once more exhibit lower v as q increases. Although the chosen v is below the speed limit, the drivers continue to exhibit behavior consistent with the uncongested regime. [ABSTRACT FROM AUTHOR]

• A tailored Bayesian optimization method for DODE problems on freeways. • A problem-specific surrogate model for high-dimensional simulation-based optimization problems. • A revised acquisition function to deal with constraints in surrogate models. This study focuses on dynamic origin–destination demand estimation problem on freeway networks. Existing studies on this problem rely on high-coverage of traffic measurements and assumptions on travel times, exhibiting limitations in real-world applications. We formulate the problem as a bi-level programming model, where micro-simulations are incorporated to precisely model traffic flows/travel times on freeways. The bi-level programming model cannot provide explicit closed-form expressions for the objective function and its derivatives, and also intrinsically high-dimensional. Thus, it is highly challenging to find efficient solution algorithms. In this regard, a problem-specific and computationally efficient Bayesian optimization approach is designed. Herein, a novel surrogate model is proposed by embedding a physical surrogate model (it characterizes underlying physical mechanisms and provides global yet less precise approximations) into a functional surrogate model (it provides precise local approximations). The embedding provides problem-specific knowledge for the surrogate model. More importantly, it also restricts the feasible region, enabling the surrogate model to efficiently deal with high-dimensional problems. Gaussian process can be served as the functional surrogate model. Two linear physical surrogate models are proposed to capture interactions between travel demand and traffic measurements. To deal with constraints in the surrogate model, a projection-distance based acquisition function is designed. In searching for new points, the proposed acquisition function is capable of assigning unique weight of exploration to each feasible solution. The proposed approach is validated based on a freeway corridor example, which indicates its outperformance over existing dynamic origin–destination estimation methods in terms of computational efficiency and solution accuracy. [ABSTRACT FROM AUTHOR]

Lane-changing behaviors frequently occur at upstream of the urban expressway off-ramp, which possibly cause bottleneck or accident. This paper studies how in-vehicle navigation information impacts lane-changing behavior at this urban expressway diverge segments with comparison to the traditional road signs by conducting a series of driving simulator experiments, in consideration of variation of the first provision time point and traffic flow density. Firstly, the driving simulator is validated by comparing data of lane-changing position and merging gap from both field survey and simulator. Then comparison study was performed for twelve scenarios which comprise four information provision design schemes under three different traffic flow density status. Lane-changing characteristics are analyzed by selecting six indicators, which are lane-changing merging gap, lane-changing position, lane change delay, lane-changing steering angle, lane-changing deceleration, and the safe distance of lane-changing. The results show that the impact of in-vehicle navigation information on lane-changing behavior varies with traffic flow density and the time point of the first navigation information provided. The in-vehicle navigation information had significant positive impact on lane-changing safety under medium to high density condition. However, the effect is not significant under light density condition. Moreover, more improvement in operational safety and smooth could be gained when the in-vehicle navigation information is provided earlier within range of 2 km upstream of the exit gore. [ABSTRACT FROM AUTHOR]

In most traffic signal optimization problems, pedestrian traffic at an intersection receives minor consideration compared to vehicular traffic, and usually in the form of simplistic and exogenous constraints (e.g., minimum green time). This could render the resulting signal timings sub-optimal especially in dense urban areas with significant pedestrian traffic, or when two-stage pedestrian crosswalks are present. This paper proposes a convex (quadratic) programming approach to optimize traffic signal timings for an isolated intersection with one- and two-stage crosswalks, assuming undersaturated vehicular traffic condition. Both vehicle and pedestrian traffic are integrated into a unified framework, where the total weighted delay of pedestrians and vehicles at different types of crosswalks (i.e. one- or two-stage) is adopted as the objective function, and temporal and spatial constraints (e.g. signal phasing plan and spatial capacity of the refuge island) are explicitly formulated. A case study demonstrates the impacts of incorporating pedestrian delay as well as geometric and spatial constraints (e.g., available space on the refuge island) in the signal optimization. A further analysis shows that a two-stage crosswalk may outperform a one-stage crosswalk in terms of both vehicle and pedestrian delays in some circumstances. [ABSTRACT FROM AUTHOR]