Ashish Bhaskar

Fusing Loop Detector and Probe Vehicle Data to Estimate Travel Time Statistics on Signalized Urban Networks

This article presents a methodology that integrates cumulative plots with probe vehicle data for estimation of travel time statistics (average, quartile) on urban networks. The integration reduces relative deviation among the cumulative plots so that the classical analytical procedure of defining the area between the plots as the total travel time can be applied. For quartile estimation, a slicing technique is proposed. The methodology is validated with real data from Lucerne, Switzerland and it is concluded that the travel time estimates from the proposed methodology are statistically equivalent to the observed values.

Passenger Segmentation Using Smart Card Data

Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.

Estimation of Travel Time on Urban Networks with Midlink Sources and Sinks

This paper presents a methodology for estimation of average travel time on signalized urban networks by integrating cumulative plots and probe data. This integration aims to reduce the relative deviations in the cumulative plots due to midlink sources and sinks. During undersaturated traffic conditions, the concept of a virtual probe is introduced, and therefore, accurate travel time can be obtained when a real probe is unavailable. For oversaturated traffic conditions, only one probe per travel time estimation interval–-360 s or 3% of vehicles traversing the link as a probe–-has the potential to provide accurate travel time.

Analysis for the Use of Cumulative Plots for Travel Time Estimation on Signalized Network

This paper provides fundamental understanding for the use of cumulative plots for travel time estimation on signalized urban networks. Analytical modeling is performed to generate cumulative plots based on the availability of data: a) Case-D, for detector data only; b) Case-DS, for detector data and signal timings; and c) Case-DSS, for detector data, signal timings and saturation flow rate. The empirical study and sensitivity analysis based on simulation experiments have observed the consistency in performance for Case-DS and Case-DSS, whereas, for Case-D the performance is inconsistent. Case-D is sensitive to detection interval and signal timings within the interval. When detection interval is integral multiple of signal cycle then it has low accuracy and low reliability. Whereas, for detection interval around 1.5 times signal cycle both accuracy and reliability are high.

Random forest models for identifying motorway Rear-End Crash Risks using disaggregate data

This paper presents an approach to develop motorway Rear-End Crash Risk Identification Models (RECRIM) using disaggregate traffic data, meteorological data and crash database for a study site at a two-lane-per-direction section on Swiss (right-hand driving) motorway A1. Traffic data collected from inductive double loop detectors provide instant vehicle information such as speed, time headway, etc. We define traffic situations (TS) characterized by 22 variables representing traffic status for 5-minute intervals. Our goal is to develop models that can separate TS under non-crash conditions and TS under pre-crash conditions using Random Forest - an ensemble learning method. Non-crash TS were clustered into groups that we call traffic regimes (TR). Precrash TS are classified into TR so that a RECRIM for each TR is developed. Interpreting results of the models suggests that speed variance on the right lane and speed difference between two lanes are the two main causes of the rear-end crashes. The applicability of RECRIM in a real-time framework is also discussed.

Public Transport Travel-Time Variability Definitions and Monitoring

Public transport travel-time variability (PTTV) is essential for understanding the deteriorations in the reliability of travel time, optimizing transit schedules, and route choices. This paper establishes the key definitions of PTTV which firstly include all buses, and secondly include only a single service from a bus route. The paper then analyzes the day-to-day distribution of public transport travel time by using transit signal priority data. A comprehensive approach, using both parametric bootstrapping Kolmogorov-Smirnov test and Bayesian information criterion technique is developed, recommends lognormal distribution as the best descriptor of bus travel time on urban corridors. The probability density function of lognormal distribution is finally used for calculating probability indicators of PTTV. The findings of this study are useful for both traffic managers and statisticians for planning and analyzing the transit systems.

Bluetooth Vehicle Trajectory by Fusing Bluetooth and Loops: Motorway Travel Time Statistics

Loop detectors are widely used on the motorway networks where they provide point speed and traffic volumes. Models have been proposed for temporal and spatial generalization of speed for average travel time estimation. Advancement in technology provides complementary data sources such as Bluetooth Media Access Control (MAC) Scanner (BMS), detecting the MAC ID of the Bluetooth devices transported by the traveler. Matching the data from two BMS stations provides individual vehicle travel time. Generally, on the motorways, loops are closely spaced, whereas BMSs are placed a few kilometers apart. In this paper, we fuse BMSs and loops data to define the trajectories of the Bluetooth vehicles. The trajectories are utilized to estimate the travel time statistics between any two points along the motorway. The proposed model is tested using simulation and validated with real data from Pacific Motorway, Brisbane. Comparing the model with the linear-interpolation-based trajectory provides significant improvements.

Average Travel Time Estimations for Urban Routes That Consider Exit Turning Movements

This paper presents a methodology for real-time estimation of exit movement–specific average travel time on urban routes by integrating real-time cumulative plots, probe vehicles, and historic cumulative plots. Two approaches, component based and extreme based, are discussed for route travel time estimation. The methodology is tested with simulation and is validated with real data from Lucerne, Switzerland, that demonstrate its potential for accurate estimation. Both approaches provide similar results. The component-based approach is more reliable, with a greater chance of obtaining a probe vehicle in each interval, although additional data from each component is required. The extreme-based approach is simple and requires only data from upstream and downstream of the route, but the chances of obtaining a probe that traverses the entire route might be low. The performance of the methodology is also compared with a probe-only method. The proposed methodology requires only a few probes for accurate estimation; the probe-only method requires significantly more probes.

Is Bus Overrepresented in Bluetooth MAC Scanner data? Is MAC-ID Really Unique?

One of the concerns about the use of Bluetooth MAC Scanner (BMS) data, especially from urban arterial, is the bias in the travel time estimates from multiple Bluetooth devices being transported by a vehicle. For instance, if a bus is transporting 20 passengers with Bluetooth equipped mobile phones, then the discovery of these mobile phones by BMS will be considered as 20 different vehicles, and the average travel time along the corridor estimated from the BMS data will be biased with the travel time from the bus. This paper integrates Bus Vehicle Identification system with BMS network to empirically evaluate such bias, if any. The paper also reports an interesting finding on the uniqueness of MAC-IDs.

Hybrid Model for Motorway Travel Time Estimation Considering Increased Detector Spacing

Travel time estimation and prediction on motorways has long been a topic of research. Prediction modeling generally assumes that the estimation is perfect. However good the modeling, errors in estimation can significantly weaken the accuracy and reliability of the prediction. Models have been proposed for estimating travel time from loop detector data. Generally, detectors are closely spaced (say, 500 m), and travel time can be estimated accurately. However, detectors are not always perfect, and even during normal running conditions a few detectors malfunction, with a resultant increase in the spacing between functional detectors. Under such conditions, an error in the travel time estimation is significant and generally unacceptable. This research evaluated the in-practice travel time estimation models during various traffic conditions. Existing models fail to estimate travel time accurately under large detector spacing and during shoulder congestion periods. To address this issue, an innovative hybrid model that considered loop data for travel time estimation was proposed. The model was tested with simulation and was validated with real Bluetooth data from the Pacific Motorway in Brisbane, Queensland, Australia. Results indicate that during non-free-flow conditions and larger detector spacing, the proposed model provides significant improvement in the accuracy of travel time estimation.