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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]

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]

A valuable consideration is inserted into car-following theory to investigate the difference of drivers' anticipation. Furthermore, a fresh macro traffic model involving the difference of the drivers' anticipation is deduced from the car-following model by applying transformation relationship from micro to macro variables. And this effect resulted from the difference of the drivers' anticipation is observed via numerical simulation. The results show that the difference of drivers' anticipation has a negligible impact on traffic flow. [ABSTRACT FROM AUTHOR]

Sound unpleasantness due to urban road traffic at crossroads was investigated through a listening test performed in a laboratory environment. Thirty-two sound sequences were created. Four factors were studied: sound level, type of crossroads, traffic density and traffic composition. The results showed that sound unpleasantness was mainly influenced by sound level. Decreasing traffic density by a factor of 2 also reduced sound unpleasantness. The roundabout was judged less unpleasant than the crossroads with traffic lights, when traffic density was high. This outcome may be explained by differences in traffic flow dynamics between the crossroads with traffic lights and the roundabout: on the one hand, for the crossroads with traffic lights, the traffic flow resembled a constant pulsed flow, with alternate cycles of acceleration/deceleration; on the other hand, for the roundabout, the traffic flow was more similar to a constant fluid flow, with less marked cycles of acceleration/deceleration. For the roundabout only, sound unpleasantness increased with the presence of heavy vehicles (or buses). Actually, sound sequences with only light vehicles were rated less unpleasant for the roundabout than for the crossroads with traffic lights. This could be due to differences in their spectral pattern. In particular, for traffic-light scenarios, higher levels within the frequency range [400, 3000] Hz occurred when vehicles passed by, in connection with higher vehicle speeds, and higher levels in the high frequencies (around 16 kHz) occurred more often when vehicles stopped, indicating the presence of braking noise with a shrill character. [ABSTRACT FROM AUTHOR]

Abstract: Although various approaches have been proposed for modeling day-to-day traffic flow evolution, none of them, to the best of our knowledge, have been validated for disrupted networks due to the lack of empirical observations. By carefully studying the driving behavioral changes after the collapse of I-35W Mississippi River Bridge in Minneapolis, Minnesota, we found that most of the existing day-to-day traffic assignment models would not be suitable for modeling the traffic evolution under network disruption, because they assume that drivers’ travel cost perception depends solely on their experiences from previous days. When a significant network change occurs unexpectedly, travelers’ past experience on a traffic network may not be entirely useful because the unexpected network change could disturb the traffic greatly. To remedy this, in this paper, we propose a prediction–correction model to describe the traffic equilibration process. A “predicted” flow pattern is constructed inside the model to accommodate the imperfect perception of congestion that is gradually corrected by actual travel experiences. We also prove rigorously that, under mild assumptions, the proposed prediction–correction process has the user equilibrium flow as a globally attractive point. The proposed model is calibrated and validated with the field data collected after the collapse of I-35W Bridge. This study bridges the gap between theoretical modeling and practical applications of day-to-day traffic equilibration approaches and furthers the understanding of traffic equilibration process after network disruption. [Copyright &y& Elsevier]

Abstract: The paper investigated drivers’ speed behaviour in a section of a rural highway crossing a small urban community in the existing scenario without any traffic calming device and in two different design scenarios with traffic calming in the urban community. Two gateways and four integrative traffic calming devices along the route within the urban area were tested. The gateways were aimed at slowing down the vehicles entering in the built-up area, while the traffic calming devices were aimed at complementing the gateway effect inside the built-up area. Two design options were tested: first option (alt1) is a combination of low cost measures, whereas the second option (alt2) is more expensive as includes a chicane and requires land acquisition. Drivers’ behaviour was investigated by means of a driving simulator experiment. The VERA dynamic-driving simulator operating at the TEST Road Safety Laboratory located in Naples (Italy) was used. Simulation results were validated by the comparison of speed behaviour in the real world and in the driving simulator, in the scenario without traffic calming. Analysis of the driving simulator experiment results was performed using two different approaches: (a) explorative description of data by cluster analysis; (b) inferential procedures about population using statistical tests. Cluster analysis was carried out in order to test if the drivers’ speed behaviour in the different design alternatives was substantially different. Statistical tests were performed in order to verify if speeds in specific sections were significantly different. Cluster analysis looked at speed profiles, whereas statistical tests looked at speed data in specific points. The obtained results showed a different behaviour of drivers approaching the urban community in the existing scenario and in the design scenarios. In the south direction, mean speed reduction ranging between 16 and 17km/h, with 5% level of significance, was observed. In the north direction, mean speed reduction equal to 11km/h, with 10% level of significance, was observed. Differences between the two design alternatives were not statistically significant. Along the urban community, a statistically significant mean speed reduction ranging between 9 and 15km/h was observed in the south direction. In the north direction, speed reduction was not statistically significant. Overall, combined results of cluster analysis and statistical tests showed that the treatments were more effective in the direction with higher speeds in the base scenario. [Copyright &y& Elsevier]

Abstract: The kinetic theory for traffic flow equations can be approached using the Grad’s method. This method, which is derived from the kinetic gas theory, was developed for the Paveri-Fontana equation when a special desired velocity model is assumed. A closure relation for the set of macroscopic equations is found when the density, the average velocity and the velocity variance are the relevant variables chosen to describe the system. Simulation results are also shown and a qualitative comparison with other models in the literature is presented. [Copyright &y& Elsevier]

• Weigh in motion (WIM) data are analysed for evaluating fatigue of bridges. • Model uncertainties are included through a probabilistic assessment. • Existing fatigue load models fail in representing the actual load effect. • A new fatigue load model is developed that agrees well with WIM data. • The new model can easily be calibrated with WIM data from other roads. Fatigue verification of bridge structures requires information on the loads induced by heavy vehicles, which can be obtained from weigh in motion measurement (WIM) systems. The current fatigue load models applied in Europe are based on traffic load measurements in 1986. This paper evaluates the appropriateness of these models for today's traffic by comparing their load effects with those of recent WIM databases, covering the years 2008 to 2018. A procedure is derived to determine the required size of the WIM database for a sound representation of the fatigue loads. The effects of traffic jams are evaluated and the required safety margins or partial factors are derived. As already concluded by others, it appears that the most frequently used fatigue load model is unable to represent the fatigue action effects of today's European traffic. In addition, the paper demonstrates that the other fatigue load models are also inaccurate. A new fatigue load model is proposed that is easy in use and gives a significant improvement in accuracy compared to the existing models. The parameters in this model can easily be calibrated for other WIM databases. [ABSTRACT FROM AUTHOR]

• A hybrid observer merging PWSL modeling and Kalman filter estimator is proposed • Two new kNN-based mechanisms for detecting road traffic congestion are designed. • Measurements from the four-lanes SR-60 freeway in California are used for validation. • The detection results show the superior performance of the new coupled kNN-ES mechanism. A systematic approach for monitoring road traffic congestion is developed to improve safety and traffic management. To achieve this purpose, an improved observer merging the benefits of a piecewise switched linear traffic (PWSL) modeling approach and Kalman filter (KF) is introduced. The PWSL-KF observer is utilized as a virtual sensor to emulate the traffic evolution in free-flow mode. In the proposed approach, residuals from the PWSL-KF model are used as the input to k-nearest neighbors (kNN) schemes for congestion detection. Here, kNN-based Shewhart and exponential smoothing schemes are designed for discovering the traffic congestions. The proposed detectors merge the desirable properties of kNN to appropriately separating normal from abnormal features and the capability of the monitoring schemes to better identify traffic congestions. In addition, kernel density estimation has been utilized to set nonparametric control limits of the proposed detectors and compared them with their parametric counterparts. Tests on traffic measurements from the four-lane State Route 60 in California freeways show the effectiveness of the PWSL-KF-based kNN methods in supervising traffic congestions. [ABSTRACT FROM AUTHOR]