Traffic Flow Levels Research Articles

Statistical and neural classifiers to detect traffic operational problems on urban arterials

This paper proposes using artificial neural networks in a modular architecture to assist in detecting different types of operational problems on signalized urban arterials. A traffic surveillance infrastructure that includes inductive loop detectors on intersection approaches as well as mid-block system loops for traffic monitoring is used. For arterials, problems that require the attention of a traffic management center operator include lane-blocking incidents, special event conditions, and detector malfunctioning. Problem detection depends on factors such as operating conditions, configuration of sensors within the network, and block or link length. The feasibility of training and testing neural network models as components of a modular architecture, with an appropriate model for each sub-problem of pattern recognition, is demonstrated. The performance of this modular architecture exceeded that of any single architecture applied to the detection of different types of operational problems. The paper also reports on the performance of each type of model considered and the effect traffic flow levels and detector configuration have on the performance of the incident detection model. The results show that with the selection of a suitable architecture, the modular neural classifier outperforms alternative discriminant analysis-based classifiers. This is demonstrated using cyclic data from microscopic simulation and field data from Urban Traffic Control System (UTCS) implementations in the Cities of Los Angeles and Anaheim, California.

Genetic algorithms in the design of complex distribution networks

Reviews the value of network concepts as a means of portraying complex logistics and distribution systems. Reports on research which focuses on the broader issues of model formulation and solution techniques rather than specific applications. Addresses the issues of designing networks with a tree structure, and also more general ones in which loops are allowed and redundancy enforced. The decision variables involved are related to whether or not a link should exist between two specific pairs of nodes, and then what should be the level of traffic flow on that particular link. Describes the design problem in detail and possible models that could be used to represent it. Follows with a description of genetic algorithms to solve the synthesis problem of deciding the node‐link topology, and the use of linear and non‐linear programming to solve the problem of assigning traffic flow to a network with a given typology in a least‐cost manner. Concludes with a description of computational experience with solving such problems.

Negative effects of mid-block speed control devices and their importance in the overall impact of traffic calming on the environment

The purpose of this study is to investigate previously unknown effects of speed control devices on fundamental characteristics of traffic, such as vehicle headway distribution, absorption capacity and average delays to vehicles entering from driveways and pedestrian crossing opportunity. Headway data were collected at seven sites in the Sydney Metropolitan Area, under various levels of traffic flow and at various distances from the devices. The results demonstrate that although speed control devices do have some negative side-effects which are sometimes statistically significant, the magnitudes of the increase in average delays and the decrease in absorption capacities around the devices are below the level that would conceivably influence the practical crossing and merging abilities. These minor inconveniences to pedestrians and drivers are confined to the immediate vicinity of the devices and are, by far, outweighed by the benefits in terms of accident savings over the whole length of the street.

Exploration of the Breakdown Phenomenon in Freeway Traffic

Of late, there has been a considerable volume of empirical research on freeway operations during periods of high flow. Part of the motivation for this realm of research is the attractive possibility of increasing flow and speed by preventing breakdown in congested operations. This possibility has been fueled by observations of a “capacity drop,” wherein the discharge flow from the resulting queue is smaller than those observed before breakdown. The research on which this paper is based was aimed at exploring in more detail the implications of metering traffic flow at levels below that at which a breakdown occurs. From this perspective, the nature of precongestion flows is investigated empirically to determine the potential gains from such a policy. The most important contribution is an examination of the likelihood of success of such metering by establishing the probability of breakdown at various traffic flow levels. It is shown, for example, that 1-min median lane flows that are 20 percent larger than queue discharge flows have only a 10 percent probability of breakdown and that 1-min flows equal to the mean queue discharge flows have a negligible probability of breakdown. These results not only have practical significance but also are useful for improving the efficiency of freeway simulation models.

Distributed intelligent control of street and highway ramp traffic signals

The paper describes a distributed, knowledge-based system for real-time, traffic-adaptive control of traffic signals. The first of a two-stage learning process optimizes the control of steady-state traffic at a single intersection and over a network of streets. The second stage of learning deals with predictive/reactive control in responding to sudden changes in traffic patterns. The system can also display, store and retrieve information about traffic flow, intersection geometry, signalization, and performance of three- and four-leg intersections. In a design setup, it can compute the economically most advantageous geometry for an intersection, given the traffic flow and acceptable levels of service. The system has been tested in the laboratory on a range of scenarios (in terms of geometry and traffic flow) and has been found to perform as expected.

Effects of ventilation and air temperature on pollution levels in underground car parks

The relationship between the levels of CO and airborne Pb was evaluated against conditions of traffic flow, ambient air temperature and the ventilation rates in 3 underground car parks. During the study period significant variations were noted in the levels of the pollutants in each of the car parks which frequently exceeded the proposed Malaysian Air Quality Standard of 0.7 μg m−3Pb and 9 μg L−1 (8 hr average) CO for indoor environs. High correlation coefficient values were obtained between the levels of CO and traffic flow and Pb and traffic flow. High traffic flow also increased the mean air temperature inside the car parks. Reductions in the ventilation rates in the car parks caused an almost two-fold increase in the level of CO and vice versa. A simple mathematical model depicting the CO concentrations in relation to other parameters was formulated.

Improving Manual Counts of Turning Traffic Volumes at Road Junctions

Turning traffic volume data gathered manually at four intersections have been studied to determine if either Fratar, Furness, or Kruithof models could be used to design labor‐saving strategies for manual counts of traffic flows at road junctions and to detect observation errors in a given data set. Each model was used to recompute the observed turning volumes by using different abstractions of the original data as model inputs. The standard errors obtained correlate partly with the level of traffic flow and the magnitude of error‐inducing features at the observation sites. The results indicate that the models could be applied to obtain an indication of the magnitudes of observation errors in given data sets. It is also demonstrated that the models can be used to design a labor‐saving observation strategy for T‐intersections in all cases and for fourlegged intersections when reliable historical data exist. The Kruithof model was found to give the least errors, and it generally entails the fewest calculations.

The Impact of Automobile Traffic on Social Life of Street Residents

Noise, vibration and pollution caused by intensive automobile traffic not only lead to mental stress and physical suffering, but they also seem to change social life among street residents. This article analyzes the effect of traffic on social behavior and community sentiment of street residents. Major part of the data used in the study draws on interview which was taken with 400 housewives random-sampled from eight areas in Tsuchiura-city. The eight areas were chosen so as to represent different levels of traffic flow. Regression analysis was performed in order to see if there is significant correlation between traffic conditions and residents' social becavior and attitudes across these eight areas. Following are the variables used in the analysis. (1) Controlling variables ; Age, Income, Automobile-owning, and Automobileusage : (2) Traffic-conditions variables (to represent objective traffic condition and subjective evaluation of street environment by residents); Traffic-flow, Noise, Exhaust-gas, Vibration, Difficulty-in-crossing, and Difficulty-in-visiting : (3) Community variables (to represent residents' social behavior and attitudes); Outdoor-walking-hours, Neighboring, and Desire-to-settle.The result of the analysis shows that traffic interferes with residents' street crossing and makes visiting and neighboring difficult. This means that due to the traffic the residents have become less likely to visit neighbors and extend social contact. Also heavy traffic reduces residents' outdoor-walking-hours considerably, indicating that they are spending more time indoors and in cars. The consequence of this would be decrease in chances for people to meet on streets and exchange casual conversation. Moreover intensive traffic minimizes residents' desire to settle in the area, which will most possibly lead to lowering of their identity and sentiment as a member of community. In sum, the data indicate that traffic has negative effect on neighborhood cohesion and community integration. Particularly in Japanese cities where streets have played a special role in formation of city culture, it constitutes a critical problem that traffic has deprived of residents social life on streets.

Relationships between road accidents and hourly traffic flow—I: Analyses and interpretation

This research extends the investigation of the relationships between measures of accidents and traffic flow, and considers the hourly flow instead of the average daily traffic (ADT), which has already been reported. The findings of this study serve as a basis for further clarification of the interactions between various levels of traffic flow and road accidents. Eight four-lane road sections were studied during an 8-year period, providing adequate data based on carefully predefined criteria. Power functions are fitted and classified according to: (1) time-sequence analysis for each roadway section; and (2) cross sectional analysis on a one year basis. The results are presented, separately for multi and single vehicle accidents, in a matrix-format. A linear dependency was observed between the power and the logarithm of the multiple constant. This was done in a similar fashion to the previously reported study of the relationships between road accidents and ADT. The results for each type of analysis and type of accident are discussed, and three examples of a practical application are given.

Estimating the Time Costs of Highway Congestion

Previous estimates of the magnitude of highway congestion costs have employed equations relating the external cost imposed on motorists by an additional vehicle to the speed of traffic flow or the ratio of traffic flow to the maximum capacity of the road. While those equations may be accurate for rural roads and expressways, they may be less accurate on city streets where delays at intersections are a dominant factor in congestion costs. This study replaces single speed-volume equations with a traffic simulation model that replicates the queuing of vehicles at traffic lights, the dispersion of platoons of vehicles as they move from one intersection to another, and the interaction of intersecting traffic flows on an urban street network. The model is used with actual Toronto road and traffic data to produce new estimates of congestion costs on specific streets during the morning rush hour. The model produces a surprisingly high average congestion cost during the morning rush hour and a poor correlation of the results with those that would be estimated for the same traffic flows by the single equation models. The simulation technique allows the calculation of congestion costs on a street-by-street basis, generating the detailed information that would be necessary for a complex congestion pricing scheme. TRAFFIC CONGESTION IN URBAN AREAS has long been recognized as a technological external diseconomy causing serious urban problems. Highway engineering studies show that over a range of traffic flow levels observed on city streets an increase in the volume of traffic flow will reduce the speed of that flow for all motorists. While the additional driver perceives the impact of this lower speed upon himself, he is not faced with the social costs of the time lost to all other motorists as a result of his entering the road. The private cost of using the road, as perceived by the added motorist is thus below the social cost, often by a large amount. It has been shown that if the marginal external social cost of an additional vehicle-mile can be calculated and a toll is charged to all motorists equal to this marginal external cost, a Pareto improvement in the efficiency of using the road can be achieved.2 Mohring [10] has estimated the magnitude of such tolls as a function of the volume/capacity ratio of the highway, a measure of capacity utilization. Johnson [6] calculated a similar set of tolls as a function of the speed of movement of traffic on the road. Walters [15] estimated the elasticity of costs with respect to speed or time of travel. Smeed [13] calculated congestion costs as a function of speed and of the volume/capacity ratio.

Car-Following Headways on Freeways Interpreted by the Semi-Poisson Headway Distribution Model

The goal of this work is to investigate driver car-following patterns on freeways, particularly as a function of traffic flow level, using a headway distribution model. A number of authors have developed “two-component” vehicular headway distribution models that assume vehicles on a road can be divided into two groups according to whether or not they are interacting with the vehicle ahead. A model of this type, the “semi-Poisson” model proposed by Buckley, is applied to a data base consisting of 42,000 observed headways from a single lane of an urban freeway over a range of flow from 900 to 2,000 vehicles per lane per hour. A previously developed computational method allows the distribution of followers headways to be calculated directly from the observed total headway distribution by numerically solving an integral equation without introducing a parametric form for the followers distribution. The resulting followers headway distribution is found to be independent of the flow with a mean of 1.32 s and a standard deviation of 0.52 s. No statistically significant discrepancies are found between the model results and the observed data. The theoretical basis for the semi-Poisson model is discussed and compared with those of other models in order to assess the plausibility of the interpretation with respect to car following.

The effect of the oil crisis on the growth in the ownership and use of cars

Since the oil crisis of 1973, a number of studies have been made in various countries of the effects of the rise in petrol prices on the level of traffic flow, but rather fewer have attempted to delineate the complex chain of reactions within the car market set off by this impulse. We attempt to do this, using data from the UK. Since 1966 during the prediction stage of the first London Transportation Study it became obvious that low income and high income households had different rates of growth of car ownership, mainly because low income households bought cheap, old cars which vary in quantity and price differently from expensive, new cars. The Greater London Council therefore sponsored a study of car prices by age and size, starting from 1957 annually, and since the oil crisis, evaluated monthly. This has enabled us to examine the strong change in trend that had occurred, with large cars depreciating 15% per annum more than the smallest. The quantities of cars of each size registered each month are available from national statistics and this enables us to say that the previous 1% per annum increase in car size was arrested, with new cars becoming substantially smaller. A model of the car market has been developed which relates on the one hand the price distribution of cars by age, and on the other hand the price. distribution of the stock of cars owned at each household income level. Via the expenditure on car purchase at each household income level and the distribution of the length of time between purchase and resale of cars, a fully dynamic model has been developed to relate expenditure flow and stock. This enables us to test the effect of different trends on the dynamic equilibrium in the car market. The implications of the two trends noted above on the prediction of future car ownership growth are discussed, with the standstill since the oil crisis attributed to petrol prices via the split in household expenditure between purchase and use.

A Mathematical Model for Predicting the Duration of Potential Conflict Situations at Intersecting Air Routes

A mathematical model is developed for predicting the expected duration of potential conflict situations at the intersection of air routes in the en route environment. Given the intersection angle of two routes and the average flows and speeds of aircraft, the model provides an estimate of the average duration of a potential conflict and the total time the aircraft would spend in potential conflict if no preventive action were taken by controllers or pilots. The model can be used in planning air route network geometry, as well as in providing guidelines to the establishment of suitable levels of traffic flow along various routes.

A Mathematical Model for Predicting the Number of Potential Conflict Situations at Intersecting Air Routes

A mathematical model is developed for predicting the expected number of potential conflict situations at the intersection of air routes. Given the intersection angle of two routes and the average flows and speeds of aircraft, the model predicts the average number of potential conflict situations per unit of time. A concept of intersection capacity based on conflict situations is introduced, and a mathematical model of intersection capacity is developed. The models can be used in planning air route network geometry, as well as in providing guidelines to the establishment of suitable levels of traffic flow along various routes.