Time Vehicle Research Articles

Travel preferences of public transport users under uneven headways

Public transport is affected by different types of randomness which impact the reliability they offer. The source of this randomness may come, for example, from casual uncertainty sources, such as accidents or protests, or from systematic uncertainty, related to service supply, such as dwelling time or travel time between consecutive stops. In addition to these sources of variability, an extra source of uncertainty experienced by users arises due to the public transport vehicle not being immediately available to avoid a wait of uncertain length. In a frequency-based system (without schedules), even under perfect regularity, users are not sure about the next vehicle's time of arrival. This article aims to find out the impact that reliability has on travellers’ public transport alternative choice. To do so, an experiment of stated preferences is carried out, where the design characteristics are four operational attributes: speed, frequency, headway regularity, and average demand. Every scenario is randomly generated, based on the operational characteristics of the specific scenario. This means the alternatives presented resemble real-life operation and they are different between respondents. A Hybrid Discrete Choice Model was estimated, which addresses preference heterogeneity by considering two latent attitudes: punctual behaviour and crowding aversion. Overall, results indicate that headway irregularity has a significant effect on travellers’ choices, both in terms of waiting time and passenger density. This confirms this attribute should not be ignored in any public transport model, especially when it comes to evaluate projects which improve the system’s reliability but not necessary its average level of service.

Measuring Passenger Car Unit (PCU) at Four Legged Roundabout using Time Occupancy Data Collected from Drone

This study aims to measure the values of passenger car unit (PCU) at a four-legged roundabout based on the time occupancy data in complex traffic operation. Within mixed traffic, the PCUs are needed as a equivalency factor to convert various type of vehicles to a standard unit. The unit of PCU was used to determine capacity and level of service of specific the traffic condition. The composition of vehicles going through the intersection, mostly varies and each type of vehicles tends to have diverse effects on capacity and level of services. Consequently, a conversion factor from various vehicles to a standard vehicle is required. The data of this study was collected using a drone at the investigated roundabout at one of the major roundabouts in Aceh Besar, Aceh province, Indonesia. The method used was the vehicle's time occupancy, in which calculated from the average time required by each vehicle to pass through the roundabout area. The results show that the PCU values obtained is 0.16 for motorcycle, 0.59 for rickshaw, 1.07 for pickup, 1.91 for a medium vehicle, and 3.76 for the heavy vehicle. These results should be utilized for converting various type of vehicles into PCUs to estimate capacity and level of services, especially at the roundabout trafict. This results may be suitable to revise the Indonesian Traffic Code, named Indonesian Highway Capacity Manual 1997, and useful for ongoing national-level efforts to upgrade the Indonesian Highway Capacity Manual.

Location‐dependent sensing data collection and processing mechanism in vehicular network

AbstractWith the promotion of the Internet of Things, in which a growing number of objects in everyday life are able to communicate with each other, crowdsensing has attracted public attention. Among different devices, vehicles with various sensors can perform the traffic data collection tasks released by the traffic center, which is helpful for monitoring road conditions. Thus, we use crowdsensing between vehicles to improve the precision of traffic state estimation. We also propose a location‐dependent sensing task assignment mechanism for traffic data collection and a multisource data fusion model for traffic data processing in a vehicular network. First, we establish a mathematical model for the sensing task assignment considering the vehicle's time budget constraint, location, and collection task requirements. We then propose a task assignment algorithm consisting of determining the order for vehicle allocation and scheduling the optimal collection path for each vehicle, which aims to achieve maximum platform utility. Furthermore, we put forward a fusion model, including spatial fusion and temporal fusion, to better process the collected data. We use a power average operator for the spatial fusion and a temporal correlation–based data compression algorithm for the temporal fusion. The obtained simulation results validate the accuracy and correctness of our approach.

Characterizing Driver Behavior on Signalized Intersection Approaches at the Onset of a Yellow-Phase Trigger

This paper involves a field test on 60 test participants to characterize driver behavior (perception-reaction time (PRT) and stopping/running decisions) at the onset of a yellow phase. Driver behavior is analyzed for five trigger distances that are measured from the vehicle position at the start of the yellow indication to the stop bar. This paper demonstrates that the 1.0-s 85th-percentile PRT that is recommended in traffic-signal-design procedures is valid and consistent with the field observations. Furthermore, this paper clearly shows that brake PRTs are impacted by the vehicle's time to intersection (TTI) at the onset of a yellow-indication introduction. This paper also demonstrates that either a lognormal or a beta distribution is sufficient to model the stochastic nature of the brake PRT. In terms of stopping decisions, this paper demonstrates that the probability of stopping varies from 100% at a TTI of 5.5 s to 9% at a TTI of 1.6 s. This paper also indicates a decrease in the probability of stopping for male drivers when compared with female drivers. Furthermore, this study suggests that drivers 65 years of age and older are significantly less likely to clear the intersection at short yellow-indication trigger distances when compared with other age groups. The dilemma zone for the less than 40 year old group is found to range from 3.9 to 1.85 s, whereas the dilemma zone for the greater than 70 year old group is found to range from 3.2 to 1.5 s.

The GPS Navigation Message

The gps users continuously receive navigation information from the GPS Space Vehicles in the form of data bits modulated on the received signals. This information, which is computed and controlled by the GPS Control Segment, includes the vehicle's time, its clock correction and ephemeris parameters, almanacs and health for all GPS Space Vehicles, and text messages. From this information the Users compute the Space Vehicle's precise position and clock offset and less precise positions and clock offsets of Space Vehicles yet to be acquired. The GPS Navigation Message design process included numerous trade studies which weighed various representations and algorithms against variables such as message size, accuracy, update frequency, User computational requirements, and legacy for the operational GPS. Other factors such as graceful degradation and future User requirements were also considered. Finally, upon selecting the appropriate design structure, the design was fine tuned to its final form and User algorithm implementation trade–offs were performed. The representation algorithms and User algorithms were jointly tested using a simulated Space Vehicle ephemeris trajectory and Space Vehicle clock. The results of these tests demonstrate that the User models represent the simulated ephemeris and clock to within 0.01 meters with precise parameters, and to within 0.1 meters with truncated parameters.