Transportation Research Record: Journal of the Transportation Research Board

Under the authority of the Flemish Traffic Centre, a study was carried out to identify road sections that are vulnerable to major incidents. The primary objective of the study was to develop a methodology capable of rapidly scanning a large network for the most vulnerable sections. The methodology builds on a number of vulnerability indicators based on an analysis of the typical stages of an incident. With these indicators, the most important bottlenecks in the network can be selected and subjected to a closer examination. The methodology developed in the project was tested on a study area comprising the network in the corridor between the two large cities of Brussels and Ghent, Belgium. For this network, an analysis of empirical data was performed to gain insight into the extent of the reliability problems on the Flemish road network.

This paper aims to investigate the breakdown phenomenon at on-ramp merging sections of urban expressways in Japan. The breakdown events are treated here as a probabilistic phenomenon. Six merging sections at which breakdowns frequently occur are selected to be different in their geometry in relation to acceleration lane length and ramp entrance side. Three different aspects of merging capacity are recognized: the maximum outflow rate before the breakdown, the outflow rate when the breakdown starts to occur, and the outflow rates during the queue discharge period. These outflow aspects are analyzed after they are converted into an equivalent passenger car unit to eliminate the effect of heavy vehicles. Then the probability of breakdown at these sections is investigated as a function of outflow rates. The observed breakdown probabilities follow the Weibull distribution function at all sections, with close values of shape parameter and wide range values of scale parameter. The values of the scale parameter are used to investigate the effects of the acceleration lane length and entrance ramp side. Moreover, the breakdown probability is investigated as a function of the occupancy observed immediately downstream of the merging section. Last, the effects of ramp flow ratio and interval length of aggregated data are discussed.

Understanding traffic diversion due to control actions is essential to the success of advanced traffic management systems such as ramp metering, arterial signal control, and traveler information services. This paper explores the systemwide traffic diversion effect of a well-documented control algorithm on a large real-world network. By using an unconventional positive approach that models how travelers actually learn the road network experientially and make heuristic route search and choice decisions in the presence of information and decision costs, this research is able to explore traffic diversion as an emergent process, with travelers’ decision-making processes individually traced. The supply-side effect of ramp metering is modeled as capacity changes at freeway bottlenecks and metered entrance ramps. Results show that only 1% of all peak-hour travelers (6,200) on the Twin Cities (Minneapolis–St. Paul), Minnesota, network change routes after meters are deployed, even though a much larger number of them (14%) are significantly hurt by the algorithm. It is also evident that ramp meters can benefit and hurt all types of roads in a large network, including freeways, local streets, and ramps. An important conclusion with practical significance is that different behavioral assumptions could produce opposing policy recommendations in traffic operations. For instance, models with normative assumptions (e.g., DUE) lead to systematic benefit overestimation because of a particular microbehavioral mechanism.

Typically, deceleration speed-change lanes (SCLs) and exit ramp terminals on freeways are designed to allow vehicles to depart the freeway safely and efficiently. Freeway diverge areas with adequate SCL lengths should enable the exiting drivers to diverge off the freeway through traffic, decelerate to the desired ramp speed, and exit the freeway safely and comfortably. Safety problems can be expected if drivers are forced to reduce speed on the main traffic lanes or to decelerate at a very high rate. In Canada and the United States, the current design guides suggest that operation and safety could be enhanced on freeway SCLs by increasing their length. In this study, driver speed behavior at freeway diverge areas was examined by using data collected on 13 exit ramp terminals on Highway 417 in the city of Ottawa, Canada. The speed, geometric, and traffic data were collected and employed to model 85th percentile speeds and deceleration rates using linear regression analysis. The modeling attempts integrated in this study resulted in eight statistically significant predictive models at the 5% level of significance.

Stratified zone metering (SZM), a successor of zone metering, was recently implemented in the United States in the Twin Cities. SZM not only restricts metering rates subject to the freeway capacity constraints but also limits the on-ramp waiting time to a predetermined threshold. In SZM, accurate queue size estimation is crucial because inaccurate queue size can violate the maximum wait time constraint or reduce the service quality of the main-line system. Currently, a uniform and pre-calibrated regression equation is adopted for ramp queue size estimation for all ramps. It has been verified that such an estimation method may lead to outstanding estimation errors for ramp queues. In this paper, different ramp queue estimation algorithms are proposed for ramps with different categories, depending on the counting errors of queue and passage detectors. For ramps with minor counting error, queue size is estimated on the basis of flow conservation. For ramps with significant counting error on passage detectors but minor counting error on queue detectors, the flow conservation model can still be applied, but the traffic counts of passage detectors are replaced by the “green counts,” which are calculated by the ramp metering rate. For ramps with significant count errors from both queue and passage detectors, a Kalman filtering model is adopted. To verify the proposed methods, surveillance video data were used to compare actual and estimated queue sizes. Results indicate that the proposed methods greatly improve the accuracy of queue size estimation as compared with the original SZM queue size estimation.

A systemwide adaptive ramp-metering (SWARM) system is being implemented in the Portland, Oregon, metropolitan area, replacing the previous pretimed ramp-metering system. SWARM has been deployed on six major corridors and operates during the morning and afternoon peak hours. This study entails a before and after evaluation of the benefit of the new SWARM system as compared with the pretimed system using the existing data, surveillance, and communications infrastructure. In particular, the objective of this study is to quantify the systemwide benefits in relation to savings in delay, emissions and fuel consumption, and safety improvements on and off the freeway due to the implementation of the SWARM system. A pilot study was conducted for 2 weeks on a 7-mi freeway corridor in an attempt to develop a strategic design for the future regional-level study. This paper discusses the selection process of the study corridor, the experimental design, and the results obtained from the pilot study.

Freeway congestion monitoring can be based either on sampling-based methods, such as probe vehicle runs, or on continuous data from loop detector infrastructure. Sample size, in terms of the number of days sampled, affects the accuracy of sampling-based methods; detector spacing or detector density affects the accuracy of the detector-based method. This paper presents an empirical model of the effect of the two parameters—sample size and detector spacing—on the accuracy of both methods in estimating the annual average of three congestion parameters: total delay, average duration of congestion, and average spatial extent of congestion. The model is developed with data from four urban freeway corridors in California. Among other conclusions, the model predicts that to measure the congestion parameters with 10% error, 4 to 6 days’ worth of good probe vehicle data or loop detector data with half-mile spacing is needed. The proposed model facilitates comparison of the two alternatives in regard to the cost for achieving the same target accuracy. The result can also be used as a guide to determine the sample size or detector spacing in planning new congestion monitoring.

Studies were conducted with a driving simulator and laptop computers to determine the effects of displaying license plate and 10-digit telephone numbers in Amber (America's Missing: Broadcast Emergency Response) alert changeable message sign messages. The first study was conducted using the Texas Transportation Institute driving environment simulator in College Station, Texas. A laboratory study was also conducted in six cities in Texas. It was found that the average reading time for Amber alert messages with a license plate number was significantly longer than for messages without a license plate number. In addition, the majority of subjects were not able to recall the entire number. A license plate number was found to be equivalent to more than three units of information. Thus a message with a license plate number exceeds current effective message design guidelines that specify a maximum of four units of information in a message and a maximum of three units of information in a message phase. Similarly, the average reading time for messages with a 10-digit telephone number is significantly longer than for messages without a telephone number. The majority of subjects were not able to recall the entire number. A telephone number was found to be equivalent to more than three units of information. A message with a 10-digit telephone number exceeds current effective message design guidelines.

Implementation of open road tolling (ORT) can provide better performance for a toll collection facility than the use of a conventional toll plaza design. Improvements have been sought to decrease toll transaction times so that drivers experience reduced delays or none at all. Although intelligent transportation systems technology such as automatic vehicle identification in the form of electronic toll collection (ETC) is a concept that has revolutionized toll collection, with the ever-increasing traffic volumes on toll facilities, more innovative methods such as ORT are being deployed. Agencies around the world have implemented ORT, and some have integrated express (high-speed) ETC lanes in their normal plaza design. A main-line toll plaza in Orlando, Florida, was renovated following an ORT design concept. The plaza was expanded to include express ETC lanes separated from the cash payment lanes by a barrier. Data were collected before construction, during construction, and after construction was completed. Analysis showed that average reduced delays for manual cash customers equaled 49.8% and for automatic coin machine customers, 55.3%; and the speed in the express ETC lanes increased by 57%. To evaluate the benefits further, forecast scenario results from a toll plaza microsimulation model, TPSIM, were analyzed with positive conclusions. An analysis of crash data showed a 22% drop in total crashes at the plaza and a 26% drop in the plaza's area of influence. This case study provided conclusive results that implementing ORT compared with using a conventional toll facility is beneficial for improving toll collection.

A fully documented case of problem identification, simulation, implementation, and evaluation has been developed for freeway bottleneck congestion mitigation. The benefits of using simulation for assessing improvement scenarios of a congestion-generating merge between two freeways and an on-ramp in the central part of Attica Tollway in Athens, Greece, are presented. The best feasible alternative identified by two simulation programs, Integration and VisSim, was constructed, and before and after field and simulation results are available. Only 16 months passed between the initial data collection for bottleneck analysis and the 2 months of evaluation with the constructed improvement, thereby minimizing large temporal and other exogenous factors. As a result of conservative simulation assumptions, larger than forecast improvements in speed, travel times, and queue reduction were realized. The two simulators, however, provided reasonable and comparable measures of performance, which were instrumental in the decision to construct the proposed improvement.

Freeway traffic surveillance systems currently collect large amounts of traffic data, sometimes a few gigabytes per day, to support various critical traffic management center functions such as incident detection, travel time and delay estimation, and congestion management. Reliable traffic information, however, requires applying quality control measures to the collected traffic data before archiving, dissemination to the public, or use in relevant applications. This paper presents a probabilistic data-driven methodology for real-time screening of freeway loop detector data. Two complementary approaches were developed to detect abrupt temporal changes in the traffic parameters, as well as possible inconsistencies among each pair of the three traffic parameters. A real-time data screening algorithm was devised to operate in three steps. An illustrative example is presented to explain how the algorithm can be applied to real-time data screening and how observations can be diagnosed for the most likely erroneous parameters.

Merging on motorways represents one of the most difficult and dynamic maneuvers of all driving subtasks. Merging operations at uncontrolled ramp junctions have been extensively investigated. With the rapid implementation of ramp metering techniques, many junctions are now operated under ramp metering control. Little research has been done to investigate the possible effects of ramp metering on the merging operation. The research described in this paper focuses on the comparisons of merging operations under both metered and unmetered scenarios. Time series data of the merging process were collected with the Transportation Research Group instrumented vehicle and video cameras at a tapered motorway merge site. The data included accurate vehicle speed measured by laser speedometer, leading and following headway measured by radar, and driver's eye movement derived from an in-car camera. Following a comprehensive analysis of the data, it was found that merging operations under ramp control could be significantly different from free merging. Merging maneuvers may become more difficult as a result of ramp control. The equity implications of such effects are discussed. It is believed that the understanding gained from this research will be useful for the design and operation of ramp metering.

This paper presents results of a safety analysis of a freeway managed-lane strategy, that is, a time-of-day managed-lane strategy that concurrently allows use of the inner left lanes by high-occupancy vehicles (HOVs) and use of right shoulders as general purpose travel lanes during peak hours. Recent 3-year crash data and corresponding annual average daily traffic (AADT) volumes and lane-type-specific AADT volumes were identified for various lane types, including the inner left lanes for HOV only use during peak hours, general purpose lanes, right shoulder lanes, and all lanes as a whole. Negative binomial regression models were used to estimate the effect of this traffic operations system and other factors relevant to crash frequency. The negative binomial regression model analyses present no evidence that the interest factors, including the managed-lane strategy during peak hours, AADT volumes, merging and diverging influence areas, weather, light conditions, and existence of pull-off areas, affect the crash frequency when aggregated across all lanes. The variable AADT volumes in the specific analysis of general purpose lanes appear to be significant and show about a 2% increase in weekday crashes for each increase of 1,000 vehicles per day in the AADT range of 50,000 to 83,000 vehicles per day. Right shoulder specific analysis shows that motorist behaviors at the merge and diverge areas during adverse light conditions are significant and shows an increase of about 38% in crashes in these areas. The managed-lane strategy does not appear to be significant to the crash frequency in the inner left lanes for HOV, general purpose lanes, or right shoulders.

This paper describes the use of high-occupancy vehicle (HOV) lanes by inherently low-emission vehicles (ILEVs) and hybrid vehicles. On the basis of the provisions of the Transportation Equity Act for the 21st Century, at least 10 states approved legislation allowing ILEVs to use HOV lanes without meeting the vehicle-occupancy requirements. The Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU) allows states also to exempt qualifying hybrid vehicles from vehicle-occupancy requirements. Since the passage of SAFETEA-LU, FHWA has granted conditional approvals to Arizona, California, and New York for hybrid use of some or all HOV lanes in those states. Contrary to federal legislation, hybrid vehicles have been allowed to access HOV lanes in Virginia since 2000. As described in this paper, few ILEVs are in operation, and thus use of HOV lanes by ILEVs is minimal. On the basis of the experience in Virginia, California, and New York, however, owners of hybrid vehicles are using HOV lanes in those states.

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.