Variability of Travel Times on Arterial Links: Effects of Signals and Volume (original) (raw)
Related papers
In arterial networks, traffic flow dynamics are driven by the presence of traffic signals, for which precise signal timing is difficult to obtain in arbitrary networks or might change over time. A comprehensive model of arterial traffic flow dynamics is necessary to capture its specific features in order to provide accurate traffic estimation approaches. From hydrodynamic theory, we model arterial traffic dynamics under specific assumptions standard in transportation engineering. We use this flow model to develop a statistical model of arterial traffic. The statistical approach is essential to capture the variability of travel times among vehicles: (1) the delay experienced by a vehicle depends on the time when it enters the link (in relation to the signal green/red phases) and this entrance time can occur at any random time during the cycle and (2) the free flow speed of a vehicle depends both on the driver and on external factors (jaywalking, double parking, etc.) and is another source of uncertainty. These two sources of uncertainty are captured by deriving the probability distribution of delays (from hydrodynamic theory) and modeling the nominal free flow travel time as a random variable (which encodes variability in driving behavior). We derive an analytical expression for the probability distribution of travel times between any two locations on an arterial link, parameterized by traffic parameters (cycle time, red time, free flow speed distribution, queue length and queue length at saturation). We validate the model using probe vehicle data collected during a field test in San Francisco, as part of the Mobile Millennium system. The numerical results show that the new distribution derived in this article more accurately represents the actual distribution of travel times than other distributions that are commonly used to represent travel times (normal, log-normal and Gamma distributions). We also show that the model performs particularly well when the amount of data available is small. This is very promising as the volume of probe vehicle data available in real time to most traffic information systems today remains sparse.
A hydrodynamic theory based statistical model of arterial traffic
In arterial networks, the dynamics of traffic flows are driven by the presence of traffic signals. A comprehensive model of the dynamics of arterial traffic flow is necessary to capture the speci fics of arterial traffic and provide accurate traffic estimation. From hydrodynamic theory of traffic flow, we model the dynamics of arterial traffic under speci fic assumptions which are standard in transportation engineering. We use this flow model to develop a statistical model of arterial traffic. First, we derive an analytical expression for the spatial distribution of vehicles. This encompasses the fact that the average density of vehicles is higher close to the traffic signals because of the delays experienced by the vehicles. Second, we derive the probability distribution of total and measured delay (to be defi ned speci fically in the document). The delays experienced by vehicles traveling on a link of the network depend on the time (from the beginning of the cycle) when they enter the link. We model the probability of delay for a path between two arbitrary points on the link. The probability distribution of measured delay takes into account the sampling scheme to derive the probability of the observed delay from probe vehicles sampled uniformly in time. Finally, we use the probability distribution of delays and a model of driving behavior to derive the probability distribution of travel times between any two arbitrary points on a link. The analytical derivations are parameterized by traffic variables (cycle time, red time, model of free flow speed, queue length and queue length at saturation). The models estimates queue length (and thus congestion levels), signal parameters and variability of driving behavior. We show that the probability distributions of travel times on an arterial links are quasi-concave. The probability distributions of travel times between any arbitrary location on the link are fi nite mixture distributions where each component represents a class of vehicles depending on the characteristics of its delay. We prove that each component of the mixture distribution is log-concave, which enables the use of speci fic optimization algorithm. The distributions derived in this report are used as fundamental building block for arterial traffic estimation using sparse travel time measurements from probe vehicles used in subsequent work.
Arterial traffic signal systems, predominantly in the United States, deploy multiple signal timing plans to account for daily variability of traffic demand. Those types of traffic flow deviations should be anticipated when timing plans are designed and, therefore, serviced satisfactorily. When traffic flow patterns are no longer predictable, a predetermined time-of-day (TOD) plan may no longer be the optimal one. This research aimed to examine signal timing optimality by applying a method similar to the selection of a traffic responsive plan to recognize automatically the best timing plan suited to current traffic conditions. The proposed method attempted to determine whether the optimality of signal timing settings could have been effectively estimated when systematic detector counts of the major approach were available. The study used 4 months of data from field microwave detectors coupled with data of turning-movement counts obtained over several days. The findings show that TOD signal timing plans mainly depended on adequate data collection that best describes a specific set of traffic conditions. Thus, the designed plan was as optimal as the related traffic information was reliable, whereas a problem arose in the case of limited-availability and low-quality data. New technologies are capable of collecting and storing massive amounts of data. Even if the granularity of collected data is low, the data can be used to improve traffic performance (i.e., reduce corridor delay). This realization could be of particular importance to traffic agencies that have installed, or plan to install, new field devices. Most urban traffic signal systems in the United States deploy multiple signal timing plans to account for within-day variability of traffic demand (i.e., morning peak, midday, evening peak, off peak, and nighttime). Signal groups forming a zone or section usually operate in a coordinated manner along an urban arterial. This coordination essentially means that signal timing plans change at the same time for all signals in a given group (zone, section etc.) to facilitate vehicle progression throughout a series of signals (1). Any type of unusual circumstances, such as incidents, construction, or severe weather, causes a significant change in anticipated traffic conditions. Traffic flow patterns are no longer predictable a priori, and a predetermined time-of-day (TOD) plan may substantially underperform under these conditions. In contrast, day-today and diurnal variations in traffic volumes and patterns are typically considered to be served in a satisfactory manner by the developed plans, because these deviations should be anticipated when the plans are originally designed. Traffic responsive plan selection (TRPS) and adaptive traffic control systems designed and deployed over the past several decades were intended to provide quicker response to constantly varying traffic conditions (2). A recent application included development of a real-time weather-responsive signal control (3). These advances attempted to incorporate more robustness into designed signal timing plans. Common existing engineering practice tends to rely on limited observations of relevant traffic patterns and volumes by considering a small data set only over several weekdays. Traffic signal settings (e.g., cycle length, splits, and offsets) are fixed within each TOD period, but traffic demands may still fluctuate significantly. Examining historical volume variations in daily traffic and corresponding responsiveness of the traffic control system can assist traffic engineers in assessing deficiencies in the state of the current traffic system. Well-designed signal control settings reduce delay and unnecessary stops at intersections and thus improve traffic flow without roadway widening. Hence, a key priority for transportation agencies is to ensure demand-suitable traffic signal timings. Yet, despite readily available detector counts, many do not regularly collect, review, or assess the quality of the traffic information they use when signal timings are designed and updated. This study attempts to demonstrate the benefit of using a large set of directional sensor data to estimate day-today variations in demand and proposing a straightforward method to evaluate current performance of TOD signal plans. The proposed approach estimates how the system would perform if it deployed an adaptive-TRPS signal control logic and whether the difference in performance warrants system retiming or upgrade. The practicality of this method is reflected in reducing the time and effort required by the existing signal design-retiming practice. Therefore, the purpose of this research is to devise a methodology to assess the extent to which existing timing plans along an arterial corridor are serving observed demands in a manner that is close to optimal and thereby to provide an upper bound on the potential
Generalized Delay Model for Signalized Intersections and Arterial Streets
Transportation Research Record: Journal of the Transportation Research Board, 1997
Average delay per vehicle is the primary measure for determining the level of service at signalized intersections. This performance measure is also a major component in the calculation of average travel speed used to determine the level of service on arterial streets. The most widely used models for estimating delay at signalized intersections are those in Chapters 9 ("Signalized Intersections") and 11 ("Urban and Suburban Arterials") of the Highway Capacity Manual. This research reviewed the literature on models for estimating delay at signalized intersections to identify limitations and formulate revised models to address those limitations. Specific problems that were addressed included the inability to account for actuated-control parameters, oversaturation and variable demand, and metering and filtering by upstream traffic signals. The research team developed a generalized delay model to address these limitations and then validated the generalized model with ...