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This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies). [ABSTRACT FROM AUTHOR]

Transportation agencies are implementing traffic management strategies to improve mobility and safety on freeways. Ramp metering is a traffic management strategy deployed to mitigate congestion on freeways by using traffic signals installed at on-ramps to control and regulate vehicle entry onto the freeway mainline. Estimating the mobility benefits of ramp metering is critical not only to determine the strategy's effectiveness but also to inform the decision-making process regarding its deployment. The before-and-after approach and ramp metering shutdown experiments are conventional methods for estimating the benefits of ramp metering. These methods could overestimate or underestimate the benefits. This study aimed to estimate the expected mobility benefits of ramp metering by leveraging ramp metering downtime due to system breakdowns. Buffer index (BI), a travel time reliability measure, was selected as the performance measure. The study was based on data collected from 2016 to 2018 on a corridor with ramp metering signals (RMSs) along I-95 in Miami- Dade County, Florida. Penalized regression methods were used to identify factors that could predict the buffer indices of the freeway segment with RMSs. Factors evaluated include ramp metering operations (on/off), freeway traffic congestion levels, freeway mainline traffic speed, ramp traffic volume, and density of on-ramps and off-ramps. Results showed a 23% reduction in BIs during moderate congestion and a 28% reduction in BIs during severe congestion. Transportation agencies could use the results when evaluating RMSs operational performance and comparing their mobility impact with other alternatives. [ABSTRACT FROM AUTHOR]

Variable speed limit (VSL) can be used on freeways to manage traffic flow with the goal of improving capacity. To achieve this objective, it is necessary that both speed and density dynamics be represented accurately. In this study, to deeply understand the effectiveness of VSL control, an analytical model was developed to represent drivers' response to updated speed limits and macroscopic speed dynamical change with respect to changeable speed limits. Specifically, to model the freeway links having VSL control, the fundamental diagram (FD) was replaced with the VSL control variable in the relaxation term of the METANET. This modification led to the speed control variable appearing linearly, which is preferable for online computation. The density dynamics are based on the cell transmission model (CTM), which is introduced to estimate the transition flow among successive links with some practical constraints. It also offers flexibility in designing active bottleneck in which there is a capacity drop once feeding flow exceeds its capacity. To exploit this benefit, a modification was introduced in the FD of the density dynamics. A VSL control strategy was proposed that explicitly considers traffic characteristics at active bottleneck and its upstream-downstream segments. It can control traffic flow into any type of active bottleneck. Then, the proposed traffic dynamics with the control strategy are implemented in a freeway corridor using the model predictive control (MPC) approach. The analysis was carried out in the calibrated microsimulation model, VISSIM, within a scenario in which shock waves were present. The microsimulation model functions as a proxy for the real-world traffic system. This study reveals that, in terms of mobility, VSL is mostly effective during congestion periods. [ABSTRACT FROM AUTHOR]

Weather information is frequently requested by travelers. Prior literature indicates that inclement weather is one of the most important factors contributing to traffic congestion and crashes. This paper proposes a methodology to use real-time weather information to predict future speeds. The reason for doing so is to ultimately have the capability to disseminate weather-responsive travel time estimates to those requesting information. Using a stratified sampling technique, cases with different weather conditions (precipitation levels) were selected and a linear regression model (called the base model) and a statistical learning model [using support vector machines for regression (SVR)] were used to predict 30-min-ahead speeds. One of the major inputs into a weather-responsive short-term speed prediction method is weather forecasts; however, weather forecasts may themselves be inaccurate. The effects of such inaccuracies are assessed by means of simulations. The predictive accuracy of the SVR models show that statistical learning methods may be useful in bringing together streaming forecasted weather data and real-time information on downstream traffic conditions to enable travelers to make informed choices. [ABSTRACT FROM AUTHOR]

Travel speed ties directly to travel time, so it is an important measure for quantifying arterial performance. However, accurately estimating link travel speed for urban arterials is difficult because of traffic fluctuations and stop-and-go conditions caused by signal control. This research proposes a two-step empirical approach to effectively estimate the link-journey speeds using only advance loop detector outputs. The first step is to estimate the spot speed on the basis of advance loop measurements using Athol's algorithm. The robust regression technique can be used to calibrate the speed estimation parameter (or -factor) in Athol's algorithm. The second step is to use the proposed simplified speed estimation model to estimate the link speed using only the calculated loop-spot speed without any knowledge of signal timing plans. Traffic operations in the central business district of the City of Bellevue, Washington, are simulated in the VISSIM traffic simulation model. The test results show that only 50 cycles of data are needed to calibrate the -factor in loop-speed estimation and the same datasets can be used to calibrate the proposed link-speed model. Using this model, the average mean absolute error over the study links is reduced from 4.24 to 1.51 mph. With proper calibration, this average error can be further reduced to 0.91 mph. The results are encouraging and satisfactory. The results also show that the accuracy of speed estimation may be further increased when more data are applied for calibration. [ABSTRACT FROM AUTHOR]

In this paper, a geometric model for predicting locations and times where sun glare has the potential to affect drivers on roadways was developed. Existing or prospective highway alignments can be considered. The method includes appropriate geometric models of the roadway alignment, astronomical computations for the relative position of the sun in the sky, and corrections for atmospheric refraction. Novel graphical tools were introduced to indicate the results of the model, which include sets of time and location where sun glare should be expected. The hypothesis that sun glare has an effect on congestion was tested by comparing real speed data from locations and times at which the geometric model indicates the presence of sun glare with those at which it does not. Sun glare is shown to have a statistically significant impact on mean traffic speeds. [ABSTRACT FROM AUTHOR]

The ability to measure or estimate accurate speed data are of great importance to a large number of transportation system operations applications. Estimating speeds from the widely used single inductive loop sensor has been a difficult, yet important challenge for transportation engineers. Based on empirical evidence observed from sensor data collected in two metropolitan regions in Virginia and California, this research developed a Kalman filter model to perform speed estimation for congested traffic. Taking advantage of the coexistence of dual loop and single loop stations in many freeway management systems, a calibration procedure was developed to seed and initiate the algorithm. Finally, the paper presents an evaluation that illustrates that the proposed algorithm can produce acceptable speed estimates under congested traffic conditions, consistently outperforming the conventional g-factor approach. [ABSTRACT FROM AUTHOR]

The nature of congested traffic flow has been not fully understood until now. An isolated bottleneck on the expressway generally influences a limited segment length, which means attenuation effects of the bottleneck on traffic stream by a distance within the influence area. However, such effects of bottleneck on speed-flow relationship have been inadequately emphasized. This paper investigates the location effects of a bottleneck on the speed-flow relationship. With consecutive 5 weekday median lane data collected by dual loop detectors on a congested expressway in Shanghai, integral speed-flow relationships including upper limb, lower limb, and capacity area are obtained from six sites. Further analyzing those sites on the same basic expressway segment unveils that it is the movement of the upper limb and lower limb that produces the integral curve. The right end of the upper limb gets an increase speed in with the site farther to its upstream bottleneck, whereas the lower limb gets a drop in speed with the site closer to its downstream bottleneck. Such a movement feature is in agreement with general driving behaviors of discharging or approaching a bottleneck. The factors intensifying or weakening the movement tendency are also identified. [ABSTRACT FROM AUTHOR]

Traffic monitoring sites are installed in highways to collect speed, volume, and classification data to support various planning and intelligent transportation system applications. There is minimal research that addresses speed accuracy and methods to evaluate it without manually collecting vehicle speed data. Accurate speeds are important because the measured speed is used to calculate axle spacing distances and determine the vehicle classification. If the speed measurements are not accurate, the axle spacing distances may not conform to expected values making it difficult to classify the vehicle. This paper identifies values for the drive tandem axle spacing on Federal Highway Administration (FHwA) Scheme F Class 9 vehicles based on truck-manufacturer sales data. A relationship between the drive tandem axle spacing and speed calibration accuracy is verified by collecting and analyzing data from two weigh-in-motion (WIM) sites in Indiana. Based on this relationship, a traffic monitoring lane can be automatically calibrated by looking at historical drive tandem axle spacing measurements. This metric was utilized by the Indiana DOT to identify WIM lanes that required recalibration. [ABSTRACT FROM AUTHOR]

This paper presents an analytical methodology to increase the accuracy of speed estimates from freeway traffic detectors by integrating information across lanes. Typically these estimates are quite noisy due to unobserved variables and measurement errors. Yet most traffic-monitoring applications employed by operating agencies only require a single measurement per direction at a detector station. Earlier efforts have focused on improving speed estimates of the individual lanes and then using a simple average to arrive at a single measure of speed at the detector station. But simply averaging the data across lanes is likely to preserve a measurement error in any one of the lanes. This paper improves speed estimates on a lane-by-lane basis using conventional aggregated flow and occupancy data obtained from single loop detectors. It then goes further by exploiting the information in the adjacent lanes to eliminate noise. In the course of analysis, data cleaning tools have been developed to identify and exclude malfunctioning detectors and transient errors, improving the validity of traffic data before estimating speed. These data cleaning tools consist of threshold value tests and basic traffic flow theory principles. To reduce the vulnerability to common transient measurement errors in individual lanes, the median of the improved estimates across lanes is taken in each direction. The methodology is then extended to clean noisy speeds measured from dual loop detectors and other sensors that mimic single loops. [ABSTRACT FROM AUTHOR]