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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]

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]

• 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]