Geoff Rose

Mobile Phones as Traffic Probes: Practices, Prospects and Issues

Abstract The provision of road‐based travel‐time information often relies on speed data collected from inductive loops imbedded in the pavement. While inductive loops are commonly installed on urban freeways, they are neither configured nor ideally located to provide speed data on arterial roads. Dissemination of dynamic, network‐wide travel information to road users is, therefore, likely to require alternative data collection techniques. This review considers the state of practice in relation to using mobile phones as traffic probes, assesses the prospects for this data collection option and identifies unresolved issues that may have implications for obtaining real‐time traffic information using mobile phones as probes. The use of mobile phones as traffic probes is appealing because the necessary infrastructure is already in place in most urban areas. Traffic speed information can be obtained by passively monitoring data transmission in the mobile phone network. International experience provides encouraging signs about the potential of mobile phones as traffic probes. Issues still to be resolved include potential public concerns about privacy, growing awareness of the road safety implications of mobile phone use and the need to understand better the quality of the data obtained from mobile phone probes.

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbournes freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the models fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways.

Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction

Neural networks have been employed in a multitude of transportation engineering applications because of their powerful capabilities to replicate patterns in field data. Predictions are always subject to uncertainty arising from two sources: model structure and training data. For each prediction point, the former can be quantified by a confidence interval, whereas total prediction uncertainty can be represented by constructing a prediction interval. While confidence intervals are well known in the transportation engineering context, very little attention has been paid to construction of prediction intervals for neural networks. The proposed methodology in this paper provides a foundation for constructing prediction intervals for neural networks and quantifying the extent that each source of uncertainty contributes to total prediction uncertainty. The application of the proposed methodology to predict bus travel time over four bus route sections in Melbourne, Australia, leads to quantitative decomposition of total prediction uncertainty into the component sources. Overall, the results demonstrate the capability of the proposed method to provide robust prediction intervals.

Enhancing the safety of pedestrians during emergency egress: Can we learn from biological entities?

It may be possible to use nonhuman biological entities for empirical study of pedestrian crowds under emergency conditions. A literature review is used to examine how the study of mass movement of organisms might enhance the safety of pedestrians during emergency egress. Recent findings from experiments with panicking ants are presented as examples, with two scenarios, of how such experiments can be used as a basis for the design of solutions to ensure safe egress of pedestrians in emergencies. Although the experiments are still in progress and it is too early to draw definitive conclusions with statistical significance, some preliminary results show promise in using ants to test models for pedestrian traffic in emergency conditions. Because of the lack of complementary data during emergency or panic-inducing situations, experiments such as these with ants provide alternate empirical ways to test whether designs developed by means of mathematical models may actually be efficacious and improve the safety of pedestrians.

Effect of Surrounding Traffic Characteristics on Lane Changing Behavior

Lane changing maneuvers could have a substantial influence on traffic flow characteristics as a result of their interfering effect on surrounding vehicles. The interference effect of lane changing is more pronounced when heavy vehicles change lanes compared to when passenger cars undertake the same maneuver. This is due to the physical effects that the heavy vehicles impose on surrounding traffic. This paper investigates and compares the traffic flow characteristics which influence the lane changing behavior of heavy vehicle and passenger car drivers on freeways under heavy traffic conditions. A trajectory data set comprising 28 heavy vehicle and 28 passenger car lane changing maneuvers is analyzed in this study. The results suggest a substantial difference exists between the traffic characteristics influencing the lane changing behavior of heavy vehicle and passenger car drivers. Heavy vehicles speed changes little during a lane changing maneuver. Heavy vehicle drivers mainly move into the slower lanes to prevent obstructing the fast moving vehicles which approach from the rear. However, passenger car drivers increase their speed according to the speeds of the lead and lag vehicles in the target lane. They more commonly move into the faster lanes to gain speed advantages.

An Integrated Framework to Predict Bus Travel Time and Its Variability Using Traffic Flow Data

Information about bus travel time and its variability is a key indicator of service performance, and it is valued by passengers and operators. Despite the important effect of traffic flow on bus travel time, previous predictive approaches have not fully considered a traffic measure making their predictions unresponsive to the dynamic changes in traffic congestion. In addition, existing methodologies have primarily concerned predicting average travel time given a certain set of input values. However, predicting travel-time variability has not received sufficient attention in previous research. This article proposes an integrated framework to predict bus average travel time and its variability on the basis of a range of input variables including traffic flow data. The framework is applied using GPS-based travel-time data for a bus route in Melbourne, Australia, in conjunction with dynamic traffic flow data collected by the Sydney Coordinated Adaptive Traffic Systems loop detectors and a measure of schedule adherence. Central to the framework are two artificial neural networks that are used to predict the average and variance of travel times for a certain set of input values. The outcomes are then used to construct a prediction interval corresponding to each input value set. The article demonstrates the ability of the proposed framework to provide robust prediction intervals. The article also explores the value that traffic flow data can provide to the accuracy of travel-time predictions compared with when either temporal variables or scheduled travel times are the base for prediction. While the use of scheduled travel times results in the poorest prediction performance, incorporating traffic flow data yields minor improvements in prediction accuracy compared with when temporal variables are used.

Lane changing models: a critical review

Abstract Lane changing maneuvers have a significant impact on macroscopic and microscopic characteristics of traffic flows due to the interference effect they have on surrounding traffic. Understanding the factors which affect drivers lane changing behavior is important due to the implication of lane changing models in variety of traffic and transportation studies. This paper reviews the existing lane changing models and assesses the strengths and weaknesses of each model type. In addition, the lane changing models are classified according to their characteristics. Then, limitations of the existing lane changing models are identified. Finally, the findings and conclusions of the paper are summarized and more promising research directions are suggested.

Lane-Changing Decision Model for Heavy Vehicle Drivers

Lane-changing maneuvers of heavy vehicles have significant influence on surrounding traffic characteristics because of the physical effects that the heavy vehicles impose on surrounding vehicles. These effects are the result of heavy vehicles’ length, size, weight, and limitations in their maneuverability. Because of the importance of heavy vehicle lane-changing maneuvers, this article presents an exclusive fuzzy logic lane-changing decision model with 2 and 3 fuzzy sets for heavy vehicle drivers on freeways. To examine the accuracy of the heavy vehicle drivers’ lane-changing decision model, the authors compared the number of estimated lane-changing maneuvers of heavy vehicles with the observed number of heavy vehicle lane-changing maneuvers. In addition, the authors compared estimated traffic flow measurements (e.g., traffic flow, average speed) with the observed traffic flow measurements through microscopic traffic simulations. Results show that the fuzzy logic heavy vehicle lane-changing model can better replicate the observed lane-changing decisions of heavy vehicle drivers than can the current lane-changing decision models. Furthermore, using an exclusive heavy vehicle lane-changing decision model can increase the accuracy of the microscopic traffic simulation software in estimating the macroscopic traffic flow measurements.

Modeling the Lane-Changing Execution of Multiclass Vehicles Under Heavy Traffic Conditions

In general, existing lane-changing behavior models focus on drivers lane-changing decisions and neglect lane-changing execution. However, a lane-changing maneuver is likely to require several seconds for execution. Excluding lane-changing execution may have a significant effect on estimated traffic flow characteristics, particularly under heavy traffic conditions (level of service E). Acceleration and deceleration behaviors of heavy vehicle and passenger car drivers during the execution of a lane-changing maneuver are compared and contrasted. In addition, separate acceleration and deceleration models are developed for heavy vehicles and passenger cars during the lane change. The vehicle trajectory data set used in this research reflects heavy traffic conditions. Analysis of heavy vehicle drivers lane-changing execution reveals that drivers maintained an almost constant speed during the maneuver; this suggests that they did not accelerate or decelerate to adjust their speeds according to the speed of surrounding traffic in the target lane. However, passenger car drivers do accelerate to adjust their speeds according to the speeds of the lead and lag vehicles in the target lane. The results highlight differences in the behavior of heavy vehicle and passenger car drivers during lane-changing execution.

Consequence of turning movements in pedestrian crowds during emergency egress

Collective egress comes into play during emergencies such as natural disasters or terrorist attacks, when rapid egress is essential for escape. An important aspect of collective egress under emergency conditions is the turning movement when a sudden change in the direction or the layout of the escape area occurs. Previous case studies of crowd disasters have highlighted the importance of such turning movements; however, both qualitative and quantitative studies seldom address this phenomenon specifically for emergency and panic situations. The paucity of complementary data on human panic presents a considerable challenge to undertaking quantitative analysis. The study described in this paper uses empirical data from real-life video footage of a crowd stampede and from panicking ants, paired with a simulation model, to demonstrate how potential problems and consequences of turning movements during collective dynamics can be studied. With this modeling tool, it may be possible to develop evacuation strategies and design solutions that can prevent stampedes and trampling, which occur when large groups of people try to escape from confined spaces where escape path directions abruptly change.