Urban Traffic Intersection Research Articles

Prediction of PM 10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration

At urban traffic intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly during the green-light period. The changes of driving patterns (i.e., idle, acceleration, deceleration and cruising patterns) generally produce uncertain emission. Additionally, the movement of pedestrians and the influence of wind further result in the random dispersion of pollutants. It is, therefore, too complex to simulate the effects of such dynamics on the resulting emission using conventional deterministic causal models. For this reason, a modified semi-empirical box model for predicting the PM 10 concentrations on roadsides is proposed in this paper. The model constitutes three parts, i.e., traffic, emission and dispersion components. The traffic component is developed using a generalized force traffic model to obtain the instantaneous velocity and acceleration when vehicles move through intersections. Hence the distribution of vehicle emission in street canyon during the green-light period is calculated. Then the dispersion component is investigated using a semi-empirical box model combining average wind speed, box height and background concentrations. With these considerations, the proposed model is applied and evaluated using measured data at a busy traffic intersection in Mong Kok, Hong Kong. In order to test the performance of the model, two situations, i.e., the data sets within a sunny day and between two sunny days, were selected to examine the model performance. The predicted values are generally well coincident with the observed data during different time slots except several values are overestimated or underestimated. Moreover, two types of vehicles, i.e., buses and petrol cars, are separately taken into account in the study. Buses are verified to contribute most to the emission in street canyons, which may be useful in evaluating the impact of vehicle emissions on the ambient air quality when there is a significant change in a specific vehicular population.

Impacts of Radio Access Protocols on Cooperative Vehicle Collision Avoidance in Urban Traffic Intersections

This paper p resents the design of a Cooperative Collision Avoidance application, and evaluates its performance with DSRC-recommended 802.11 Medium Access Control (MAC) and with a novel Vehicular Self-Organizing MAC (VeSOMAC) protocol. VeSOMAC is designed as a fully distributed TDMA protocol that relies on an in-band control exchange technique for autonomous TDMA slot allocation among vehicle-mounted wireless communication modules. A hybrid traffic and wireless network simulator has been developed for evaluating both network level and application level performance in the presence of different wireless access protocols. Detailed network and vehicular traffic simulation models have also been developed for evaluating a Cooperative Collision Control (CCA) application, operating in urban traffic intersection scenarios. Simulation results demonstrate that unlike the 802.11 style contention based protocols, a schedule based protocol such as VeSOMAC can offer better vehicle safety performance through smaller and bounded packet latency in vehicular ad hoc networks.

Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at urban traffic intersection during winter period

There are several models that can be used to evaluate roadside air quality. The comparison of the operational performance of different models pertinent to local conditions is desirable so that the model that performs best can be identified. Three air quality models, namely the ‘modified General Finite Line Source Model’ (M-GFLSM) of particulates, the ‘California Line Source’ (CALINE3) model, and the ‘California Line Source for Queuing & Hot Spot Calculations’ (CAL3QHC) model have been identified for evaluating the air quality at one of the busiest traffic intersections in the city of Guwahati. These models have been evaluated statistically with the vehicle-derived airborne particulate mass emissions in two sizes, i.e. PM10 and PM2.5, the prevailing meteorology and the temporal distribution of the measured daily average PM10 and PM2.5 concentrations in wintertime.The study has shown that the CAL3QHC model would make better predictions compared to other models for varied meteorology and traffic conditions. The detailed study reveals that the agreements between the measured and the modeled PM10 and PM2.5 concentrations have been reasonably good for CALINE3 and CAL3QHC models. Further detailed analysis shows that the CAL3QHC model performed well compared to the CALINE3. The monthly performance measures have also led to the similar results. These two models have also outperformed for a class of wind speed velocities except for low winds (<1 m s−1), for which, the M-GFLSM model has shown the tendency of better performance for PM10. Nevertheless, the CAL3QHC model has outperformed for both the particulate sizes and for all the wind classes, which therefore can be optional for air quality assessment at urban traffic intersections.

A semi-empirical box modeling approach for predicting the carbon monoxide concentrations at an urban traffic intersection

Emissions generated roadside and at intersections are observed to be affected when there is a sudden change in the traffic flow pattern or increase in the vehicular population, particularly, during peak hours and during special events. The vehicles that queue up at traffic intersections spend a longer amount of time in idle driving mode generating more pollutant emissions per unit time. Other driving patterns (i.e., acceleration, deceleration and cruising) are also observed at intersections, affecting the emission pattern and therefore the resulting pollutant concentrations. The emission rate is not only affected by the increase in the vehicular population but also by the constantly changing traffic flow patterns and vehicles’ driving modes. The nature of the vehicle flows also affects the rate and nature of the dispersion of pollutants in the vicinity of the road, influencing the pollutant concentration. It is, therefore, too complex to simulate the effect of such dynamics on the resulting emission rates using conventional deterministic causal models. In view of this, a simple semi-empirical box model based on the ‘traffic flow rate’, is demonstrated in the present study for estimating the hourly average carbon monoxide (CO) concentrations on a 1-week data at one of the busiest traffic intersections in Delhi. The index of agreement for a whole week, was found to be 0.84, suggesting that the semi-empirical model is 84% error free. A value of 0.87 was found for weekdays and 0.75 for weekend days. The correlation coefficient for the whole week was found to be 0.75, with 0.78 for the weekdays and 0.62 for the weekend days. The RMSE and RRMSE were found to be 1.87% and 41% for a whole week, with 1.81% and 39.93% for the weekdays and 2.0% and 43.47% for the weekend days, respectively. Specific vehicle emission rates are optimized in this study for individual vehicle category, which may be useful in assessing their impacts on the air quality when there is a significant change in a specific vehicular population and the traffic pattern.

COOPERATIVE AND NONCOOPERATIVE BEHAVIORS AT A SIMPLE URBAN INTERSECTION

In this work we use game theory to analyze what happens at an urban traffic intersection when the vehicle flows, our players, decide to cooperate or not. We use an approximate model for the behavior of queues at a traffic intersection, to allow us study the qualitative behavior of traffic.The aim will be to minimize the queue length, with the additional assumption that the queue has a finite capacity k. The light is seen as a coordinating element, that it is not always obeyed.

Stop-Light Compliance by Cyclists: An Informal Look

To gain some further insight as to the degree of mandated (New York State, 1984) cyclists' observance of stop-lights, noted in Trinkaus' (1984) study of motor vehicle compliance with such signals, the research design was replicated one year later by observing bicycles rather than motor vehicles. The same urban traffic intersection, through which flowed four lanes of east-west and four lanes of north-south traffic, was viewed. Similarly, a total of 45 1-hr. citings of convenience were made of the north-south movement, equally divided among Tuesday, Wednesday, and Thursday, during the summer months from 1000 to 1500 hours. The timing of the traffic signal was identical: 45 sec. go (green), 4 sec. caution (yellow), and 41 sec. (red). The definition of passing the stop-light was the same: entering into and continuing through the intersection in the face of a red signal. Bicycle traffic averaged approximately 137 machines per hour, of which about 61 confronted a red-signal situation. Of these, approximately 19 went through, 41 stopped seemingly only because the pattern of vehicle cross-traffic did not permit passage, while 1 halted apparently simply because the traffic signal was red. Also, 1-hr. viewings were made at six other large intersections in other parts of the area, through which flowed approximately 140 bicycles per hour per intersection. At four of the six, no cyclist, called upon to stop when confronted with a safe passage through the intersection, did so. At the other two a total of three cyclists stopped when they could have safely proceeded. The results imply support for Trinkaus' earlier suggestion that generally cyclists disregard stop signals. It is interesting to note, however, that about half of those running a red light sounded a whistle as they did so. The practice may well stem from advice on avoiding accidents suggested by leaders in the field, such as the Editors of Bike World ( 1976).

Stop-Light Compliance: Another Look

As part of a campaign against motorists who disregard stop-lights a northeastern state enacted legislation which in effect changed the mandated monetary fines for violators from maximums (no more than) to minimums (no less than). To gain some insight as to whether this new statute seemed to have any mitigating effect on the number of transgressors, a study made by Trinkaus ( 1983), immediately prior to the enactment, was replicated approximately one year later. The same major urban traffic intersection, through which flowed four lanes of east-west and four lanes of north-south traffic, was observed. Again, a total of 45 1-hr. citings of convenience were taken of the north-south movement, equally divided among Tuesday, Wednesday, and Thursday, 1000 to 1500 hours. The timing of the traffic signal was the same as before, 45 sec. go (green), 4 sec. caution (yellow), and 41 sec. (red). The flow of traffic, however, was about 30% less: down from approximately 800 private and 1850 commercial vehicles per hour to about 560 and 1300. The same definition of passing the stop light was used: entering into and continuing through the intersection after the signal had turned red. While the results of the original inquiry suggest that on the average about 6 out of 20 drivers who had the opportunity went through a red light, the findings of this follow-on study implies that the ratio is now approximately 7 out of 20. No firm distinction was evidenced in this, or the previous, study that the stopping practices of drivers of private automobiles are especially different from those of operators of commercial vehicles. Although the violation rate is up, by about 15 percent, one should not infer that the legislation was ineffective, for in its absence the increase might have been greater. Too, it is possible that the decreased flow of traffic could also have been an abating factor: lighter traffic perhaps tending to increase drivers' tolerance to accept delays. As a considerable number of bicycles were noted during this inquiry, count was kept of the number of cyclists passing through the intersection: about 70 per hour. Of those who confronted a red light, almost all who had the opportunity to do so disregarded the stop signal. Consequently, between motorists and bicyclists, crossing at a corner with the green light is something which a pedestrian would be well advised to do with care.