Quantifying travel time variability (TTV) of buses, passenger cars, and taxis helps individuals understand the reliability of their trips. However, invalid travel time data compromises the accuracy of quantifying the variability of valid travel times. In this study, a clustering methodology, the K−2 finite mixture model (K−2FMM) based on a log-normal distribution, is presented to address the problems of identifying invalid travel times and quantifying the variability of valid travel times in the distribution. The K−2FMM approach can dynamically find an exact K value to cluster travel time data into K log-normal components to best classify the invalid and valid travel times. As a result, invalid travel times represented by component K are filtered out. Other K−1 components are used to measure the degree of variability of valid travel times and to determine some TTV indices such as mean, variance, and 90th percentile travel time. Two real cases illustrate the characteristics of period-to-period TTV for three travel modes. TTV of three travel modes are analysed and distinct taxi TTV is revealed by means of optimal K−1 components. Hence, the proposed K−2FMM approach is appropriate for researchers to more accurately quantify the variability of valid travel times.

For the purpose of accurately capturing the evolution characteristics of the congestion on urban expressways under traffic incidents, this study developed a framework model for the time-variant propagation speed and congestion boundary based on shockwave model. An initial analysis demonstrated that the traffic demand and facility supply determine the propagation speed and boundary. The outflow at the incident section was considered, which is affected by the number of closed lanes, the proportion of buses, and the number of lane-changing vehicles. Additionally, the background demand varies with the time of day and the location due to a high density of ramps. The entry and exit flows of on-ramps and off-ramps under the traffic incident were shown to have a considerable impact on the congestion propagation. Consequently, the framework model was developed and parameters were estimated based on the field data of ring-road expressways in Beijing. For practical purpose, the data inputs were provided by the floating car system and remote traffic microwave sensor system. Three incidents on expressways were adopted to evaluate the capability of the model. Results showed that the proposed model is able to practically model the time-variant characteristics of the propagation speed and congestion boundary of traffic incidents.

Different wireless communication technologies, such as wireless fidelity (Wi-Fi), 4G cellular technologies and worldwide interoperability for microwave access (WiMAX) have been used as alternatives or supplement to wired communication in intelligent transportation systems (ITS). Widespread deployment of wireless technologies in ITS require a comprehensive understanding of their performance, limitations and advantages in different field conditions. The authors evaluated performance of Wi-Fi wireless communication between adjacent roadside ITS devices (i.e., nodes) in different field conditions with varying characteristics of Wi-Fi technology. Field tests revealed that modulation rates, transmission power, line of sight, distance between nodes play critical roles in the performance of Wi-Fi communication in different roadway conditions. To achieve a desired level of performance requirements between adjacent nodes, minimum network performance thresholds must be realized in the field. Transportation agencies can identify the achievable performance, such as saturated throughput, delivery ratio and received signal strength at a particular location, by following the field test procedure developed in this research. The evaluation strategies and results presented in this study will contribute to the future planning and design of Wi-Fi communication for a roadway wireless sensor or device network considering corresponding communication performance requirements for specific ITS applications.

An analysis of pedestrian walking behavior using gait parameters is presented; it uses automated video analysis to collect pedestrian data at a signalized intersection in Nanjing, China. Two aspects of microscopic pedestrian behavior are considered: First, the walking mechanism represented by pedestrian gait parameters. Second, the non-conforming crossing behavior of pedestrians. The effect of various pedestrian related attributes are investigated. The results show high accuracy in automatically detecting pedestrian violations, with an 85.2% correct detection rate. The walking speed and gait parameters for spatial violators are found to be significantly higher compared to non-violators. It is also found that pedestrians who enter the crosswalk during the late stage of the green pedestrian phase often adopt higher walking speed. The gait analysis shows that males tend to have a higher walking speed, walk ratio and longer step length than females. Single pedestrians are found to have higher speed and step frequency compared to pedestrians in groups. The presence of bikes on crosswalks significantly decreases the pedestrian walking speed, step length and frequency, leading to more gait variability. Such results are useful for many future applications such as calibration of simulation models and violation detection.

Many transportation agencies use re-identification technologies to identify vehicles at multiple points along the roadway as a way to measure travel times and congestion. Examples of these technologies include license plate readers, toll tag transponders, and media access control (MAC) address scanners for Bluetooth devices. Recent advancements have allowed for the detection of unique MAC addresses from Wi‑Fi and wireless local area network enabled devices. This study represents one of the first attempts to measure the fundamental characteristics of Wi‑Fi re-identification technology as it applies to transportation data collection. Wi‑Fi sampling rates, re-identification rates, range, transmission success rates, and probability of discovery of sensors and mobile devices were measured, and a model of probability of detection is presented. Field tests found that mobile phones routinely experienced significant time gaps between Wi‑Fi transmissions. The study recommends that Wi‑Fi sensors be deployed at low-volume, low-speed roadways, with sensors positioned near intersections where vehicles are likely to slow or stop. Due to Wi‑Fi's relatively low probability of discovery, the technology may produce poor results in applications that require re-identifying vehicles over multiple consecutive sensors.

To help drivers safely enter the arterial road from the access road, the authors develop a crash warning system for vehicles in right-turn scenario based on DSRC (dedicated short range communications). Drivers’ right-turn behaviours from an access road to an arterial highway are considered in the system. Warning algorithms were tested with field data and DSRC on-board and roadside equipment. Corresponding outliers filter shows a reliable performance. In this system, right-turn process is divided into three phases: turn-in, keep-steady, and turn-out. Based on the field data, they establish regression models for each phase. Model results show that: (i) the overall duration of the three phases of the right-turn manoeuvre increases with the amount of cars coming from left on main artery; (ii) the amount of cars that are influenced by the arterial traffic increases the duration of first two phases, but decreases the last phase duration; (iii) the longer the first two phases last, the shorter the last phase would be; and (iv) drivers tend to decelerate before turning right when there are more than two cars coming from left.