Renatus Mussa

EVALUATION OF AN ALTERNATIVE TRAFFIC LIGHT CHANGE ANTICIPATION SYSTEM

A driving simulator was used to study the efficacy of an alternative signal phasing program. The new Traffic Light Change Anticipation System (TLCAS) utilizes a flashing amber in conjunction with a solid green indication to warn drivers of the impending onset of the solid amber indication. This new program was expected to provide drivers with additional information with which to make safe stopping or crossing decisions, and reduce behaviour associated with increased accident rates at signalized intersections. Additionally, a new measure of first response variability was introduced to evaluate the effect of the new program on driver behavior. The results indicated that the new system has the ability to reduce the number of red light violations. Of the 1148 target intersections, 43 violations were recorded for the regular program compared to 9 for the TLCAS program. The severity of decelerations was also impacted. The regular program displayed average maximum decelerations of 3.1 m/sec2 compared to 2.5 m/sec2 for the TLCAS program. However, the TLCAS program showed an increased variability in first response five times larger than the regular program. This finding, in conjunction with traditional measures, indicates that the new system performs comparably to an increased amber duration by increasing the potential for conflicting decisions between successive drivers approaching an intersection. Altogether, the results suggest that this alternative signal phasing program would not improve intersection safety. The findings also suggest that further study of the longitudinal behavior after the introduction of a TLCAS program may be warranted. Specifically, future studies should examine whether subjects engage in appropriate adaptive changes when confronted with a solid amber period warning after becoming familiar with the TLCAS.

Simulator Evaluation of Green and Flashing Amber Signal Phasing

A research study conducted to evaluate the efficacy of flashing amber signal phasing is reported. Flashing amber, which is set to overlap with the green indication a few seconds before the onset of solid amber, is a form of time reference aid used to warn drivers of an impending onset of amber. The time reference aid is a concept predicated on the principle that driver decisions will be easier and more predictable if drivers have advance information that allows them to predict the onset of amber. The evaluation of the flashing amber method showed that its implementation has the potential to reduce red-light violations, severity of maximum decelerations, and kinematically defined inappropriate stop or cross decisions. However, the data also showed that compared with the regular signal phasing, the flashing amber phasing increased the size of the indecision zone, a mechanism usually responsible for increased rear-end collisions. A measure not previously used in literature was introduced to compare the regular and the experimental signal phasing. This measure, which analyzes first-response time variability in relation to the indecision zone, predicted that increased rear-end collisions might be expected as a result of implementation of the flashing amber signal phasing. Generally, the results suggest that the implementation of a flashing amber signal phasing would not significantly increase intersection safety despite the notion that it would improve driver anticipation of the onset of solid amber.

Evaluation of Truck Operating Characteristics on a Rural Interstate Freewaywith Median Lane Truck Restriction

The increase in truck traffic on interstate freeways continues to raise concerns about the large trucks’ dimensions that cause sight distance problems and about trucks’ low capability to accelerate, decelerate, and maintain speed particularly on steep grades. To address safety and operational concerns caused by truck traffic, a multitude of restriction policies have been instituted around the United States. This study was aimed at determining the operational and safety impacts of the 24-h restriction of trucks from using the median lane of a six-lane freeway corridor, Interstate 75 in Florida. It should be noted that traffic flow on this corridor is relatively uncongested and the corridor operates at Level of Service B or better throughout the day. Analysis of field and simulation data indicated that the difference between truck and passenger car speeds and travel times were insignificant on the unrestricted middle and shoulder lanes. About two-thirds of both passenger cars and trucks were traveling within the 10-mph pace that ranged from 70 to 80 mph in the corridor, which has a speed limit posted at 70 mph. The field data also indicated that trucks were able to use the middle lane to pass 25% of the time during the truck peak-hour period with the assumption of a 10-s gap acceptance. In addition, simulation analysis indicated that opening all lanes to trucks increased the number of lane-changing maneuvers by 11% in the daytime, a phenomenon likely to increase crashes in the corridor.

Simulation assessment of incident detection by cellular phone call-in programs

This research study was designed to assess by simulation the efficacy of incident detection by cellular phone call-in programs. The assessment was conducted by varying the proportion of drivers with cellular phones on the highway so as to mirror the cellular industry statistics that show a continued growth of ownership of cellular phones in the United States. An analytical model, which combined simulation and the limited field data available in the literature, was used to determine measures of effectiveness of the cellular phone-based detection system.The results showed that a cellular phone detection system offers fast incident detection times and higher detection rates for both shoulder and lane blocking incidents. For example, in moderate traffic flow (i.e. 1,550 vehicles per hour per lane), 90 percent of incidents blocking two lanes were detected in 1.5 minutes when the proportion of drivers with cellular phones was one out of 10 drivers, even with only 20 percent of them willing to report incidents. When the current proportion of cellular ownership, i.e. 1 out of 3, was used in the simulation, the detection time improved to 0.8 minutes.The simulation analysis of incident detection by cellular phones also showed that there is a direct relationship between the probability of detection and the detection time; that is, the specification of a higher detection rate resulted in slower detection times. This is in sharp contrast with the results of field study of automatic incident detection (AID) systems which demonstrated an inverse relationship between probability of detection and detection time.

Simulation Analysis of Truck-Restricted and High-Occupancy Vehicle Lanes

Numerous studies have reported the efficacy of high-occupancy vehicle (HOV) lane restriction and truck lane restriction implemented independently, but the simultaneous use of both restrictions on an urban freeway corridor raises numerous operational and safety issues. This research study analyzed the operational and safety experience of an 83-mi corridor of I-95 in South Florida that has both HOV and truck lane restriction. Results of a field-validated VisSim simulation model showed that high-occupancy vehicles and automobiles gained significantly more travel time savings and speeds on the restricted lanes than on the general lanes. Also, vehicle queue lengths around critical merging and diverging areas increased significantly as the percentage of trucks increased. Results indicated that during peak traffic conditions right lanes had higher lane occupancy than left lanes, whereas during off-peak traffic conditions center lanes carried more vehicles per lane than the outermost lanes; these results suggest that congestion on right lanes forces automobiles to use left lanes. Furthermore, results showed that most lane changes occurred during peak traffic flow conditions—about twice that of off peak—and appreciable speed differences existed between restricted and nonrestricted lanes. Simulation results for off-peak traffic conditions did not show any noticeable changes in traffic operating characteristics resulting from lane restriction strategies. On the basis of these results, it can be fairly concluded that on urban freeways significant operational and safety benefits of the combined implementation of HOV and truck lane restrictions accrue during congested traffic conditions rather than during uncongested conditions.

MODELING DETECTION OF INCIDENTS BY DRIVERS

Abstract An analytical model was developed to assess the detection of freeway incidents by drivers using advanced communications technologies. Drivers can report incidents to a highway agency by voice or by digital messaging either directly or through roadside beacons using a fixed or portable wireless communications system such as a cellular telephone. A FRESIM model was used to simulate shoulder and lane-blocking incidents occurring under variable traffic flow rates. The proportion of drivers with an in-vehicle communication medium arriving upstream of an incident and the degree of the drivers’ reporting propensity were introduced as controlled variables. A discrete probability model was used to represent binary reporting decisions of drivers arriving upstream of an incident. The results showed that all incident types that were simulated could be detected in a short time with high probability of detection. The data indicated that all incident types, regardless of the prevailing traffic volume at the time of their occurrence, were detectable in less than one minute if one of five drivers on a freeway had an in-vehicle communication medium and was willing to use it to report the incident. Of special importance is that at the end of 1995 there was one cellular telephone user per five licensed drivers in the United States. The results of the modeling further showed that other factors affecting the detection performance of a driver-initiated incident detection process were the willingness of drivers to report incidents, the length of roadway within which an incident is visible to a driver, and the austerity of the highway agency’s procedure for mitigating false reporting.

MONITORING URBAN FREEWAY INCIDENTS BY WIRELESS COMMUNICATIONS

The efficacy of the driver-initiated incident detection system was assessed by modeling the use of new communications technologies to mitigate problems that beset current cellular call-in programs, problems such as insufficient information on incident location, false reporting, and duplicate calls. An analytical model supported by field data for several important variables was used to determine the system performance measures, that is, the detection rate, mean time to detect, and false alarm rate. The results indicate that with the implementation of new technologies that lead to the automatic geolocation of the driver initiating an incident call, the driver-initiated detection system is effective. The system achieved good detection rates and faster detection times across all simulated incidents occurring under variable traffic flow rates that ranged from light traffic to congested traffic. For instance, even when the proportion of drivers with cellular phones was 1 out of 10, with only 10 percent of them willing to use their phones to report the incidents, close to 80 percent of the incidents were detected within 2 min. This result is consistent with the results of the field studies and shows that even better detection performance can be achieved if current levels of cellular phone ownership in the United States are considered and if drivers’ reporting propensity is raised. It is noteworthy that the Cellular Telecommunications Industry Association reported that there was one cellular phone user per two licensed drivers in the United States at the end of 1999.

SITE CHARACTERISTICS AFFECTING OPERATION OF TRIPLE LEFT-TURN LANES

The growth of traffic flow in urban areas has resulted in increased installation of triple left-turn lanes with the aim of reducing vehicle delays, queue lengths, and vehicle storage bay lengths by dividing the left-turn queue demand among three lanes. The study reported here analyzed the influence of a number of geometric factors found at 15 triple left-turn lane sites in Florida on saturation flow, lane usage, and lane utilization. A total of 2,395 lane cycles and 38,023 vehicles were observed. The observed saturation flows yielded a mean saturation flow of 1,859 passenger cars per hour of green per lane (pc/h/ln) with the 95% confidence interval of 1,810 pc/h/ln to 1,907 pc/h/ln. The Fisher least significant difference test and the Hsu multiple comparison with the best test were used to determine the statistical significance of the variables’ influence on saturation flows. The results showed that triple left-turn lanes on downgrades and with an angle of turn less than 90 degrees were the two characteristics that most contributed to high saturation flow, and triple left-turn lanes located on oneway streets and on curved approaches had the lowest saturation flow. Lane utilization was dependent on the geometrics of the intersections: shadowed left-turn lanes had lower utilization of the innermost lane compared with unshadowed lanes.

SETTING UP A PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF HIGHWAY VEHICLES

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired.

60 kph Minimum Speed Limit on Rural Interstate Freeways: Is it Relevant?

The State of Florida has a practice of posting a maximum speed limit of 105 kilometers per hour (kph) and a minimum speed limit of 60 kph on rural interstate freeways. The results of safety and operational evaluation on these freeways showed that while only 0.14% of recorded vehicles had speeds below the 60 kph posted minimum sped limit, 9% of crash-involved vehicles were estimated to have speeds below 60 kph. The overrepresentation of slow moving vehicles in the crash data suggests that even a small proportion of under 60 kph vehicles can have negative implications on safety. Thus, regulation of vehicle speeds at the lower end of speed distribution is equally important. The Poisson regression modeling indicated that the difference between the 85th and 15th percentile speeds had a positive effect on crashes.