TRA-963: EVALUATION OF USING WIFI SIGNALS TO ESTIMATE INTERSECTION TRAVEL TIME

Travel time is an important measure in traffic engineering and planning with many applications including identification of network bottlenecks, plan to improve traffic mobility, providing commuters with travel time information, and traffic signal control evaluation and control. Currently a number of technologies can provide travel time information such as GPS enabled probes and identifying vehicles with Bluetooth or Wi-Fi devices. The later method detects and matches unique Media Access Control (MAC) address of the Bluetooth or Wi-Fi activated devices to calculate travel time information. This method is a non-intrusive and cost effective and has gained a lot of attention in the past few years. Extensive research has been done on evaluating the accuracy, application, and market penetration rate of using Bluetooth technology for travel time estimation for both urban arterials and highways. However, the application of Wi-Fi MAC address detection and matching for travel time estimation at urban arterials has not been adequately studied. The limited available studies are contradicting with significant differences in terms of travel time accuracy, and penetration rates. This study intended to evaluate the accuracy and reliability of using the combination of Bluetooth and Wi-Fi based travel time estimates through a case study. A sample size analysis is conducted and the expected statistical sampling errors are compared with that of obtained from comparing the Bluetooth and Wi-Fi data with ground truth information which is collected through video footage. The results of this study show that Wi-Fi signals can also provide reliable travel time information. When combined with Bluetooth travel times data, which significantly increases the market penetration rate comparing to using Bluetooth alone. The combination of using Bluetooth and Wi-Fi signal provided penetration rates up to 8% with errors less than 10% compared to ground truth data

Developing Sampling Strategies and Predicting Freeway Travel Time Using Bluetooth Data

Accurate, reliable, and timely travel time is critical to monitor transportation system performance and assist motorists with trip-making decisions. Travel time is estimated using the data from various sources like cellular technology, automatic vehicle identification (AVI) systems. Irrespective of sources, data have characteristics in terms of accuracy and reliability shaped by the sampling rate along with other factors. As a probe based AVI technology, Bluetooth data is not immune to the sampling issue that directly affects the accuracy and reliability of the information it provides. The sampling rate can be affected by the stochastic nature of traffic state varying by time of day. A single outlier may sharply affect the travel time. This study brings attention to several crucial issues - intervals with no sample, minimum sample size and stochastic property of travel time, that play pivotal role on the accuracy and reliability of information along with its time coverage. It also demonstrates noble approaches and thus, represents a guideline for researchers and practitioner to select an appropriate interval for sample accumulation flexibly by set up the threshold guided by the nature of individual researches’ problems and preferences. After selection of an appropriate interval for sample accumulation, the next step is to estimate travel time. Travel time can be estimated either based on arrival time or based on departure time of corresponding vehicle. Considering the estimation procedure, these two are defined as arrival time based travel time (ATT) and departure time based travel time (DTT) respectively. A simple data processing algorithm, which processed more than a hundred million records reliably and efficiently, was introduced to ensure accurate estimation of travel time. Since outlier filtering plays a pivotal role in estimation accuracy, a simplified technique has proposed to filter outliers after examining several well-established outlier-filtering algorithms. In general, time of arrival is utilized to estimate overall travel time; however, travel time based on departure time (DTT) is more accurate and thus, DTT should be treated as true travel time. Accurate prediction is an integral component of calculating DTT, as real-time DTT is not available. The performances of Kalman filter (KF) were compared to corresponding modeling techniques; both link and corridor based, and concluded that the KF method offers superior prediction accuracy in link-based model. This research also examined the effect of different noise assumptions and found that the steady noise computed from full-dataset leads to the most accurate prediction. Travel time prediction had a 4.53% mean absolute percentage of error due to the effective application of KF

A Kalman-filter approach for dynamic OD estimation in corridors based on bluetooth and wifi data collection

From the point of view of the information supplied by an ATIS to the motorists entering a freeway of one of the most relevant is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS, the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimating the current traffic state and forecasting its short term evolution. Travel Time Forecasting and Dynamic OD Estimation are thus two key components of ATIS/ATMS and the quality of the results that they could provide depend not only on the quality of the models but also on the accuracy and reliability of the measurements of traffic variables supplied by the detection technology. The quality and reliability of the measurements produced by traditional technologies, as inductive loop detectors, is not usually the required by real-time applications, therefore one wonders what could be expected from the new ICT technologies, as for example Automatic Vehicle Location, License Plate Recognition, detection of mobile devices and so on. A simulation experiment is proposed prior to deploy the technology for a pilot project. The simulation emulates the logging and time stamping of a sample of equipped vehicles providing real-time estimates of travel times for the whole population of vehicles and OD pattern of the equipped vehicles are considered real-time estimates of the dynamic OD pattern for the whole population of vehicles. The main objective of this paper is to explore the quality of the data produced by the Bluetooth and Wi-Fi detection of mobile devices equipping vehicles to estimate time dependent OD matrices. Ad hoc procedures based on Kalman Filtering have been designed and implemented successfully and the numerical results of the computational experiments are presented and discussed.Peer ReviewedPostprint (author’s final draft

A Kalman-filter approach for dynamic OD estimation in corridors based on bluetooth and Wi-Fi data collection

From the point of view of the information supplied by an ATIS to the motorists entering a freeway of one of the most relevant is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS, the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimating the current traffic state and forecasting its short term evolution. Travel Time Forecasting and Dynamic OD Estimation are thus two key components of ATIS/ATMS and the quality of the results that they could provide depend not only on the quality of the models but also on the accuracy and reliability of the measurements of traffic variables supplied by the detection technology. The quality and reliability of the measurements produced by traditional technologies, as inductive loop detectors, is not usually the required by real-time applications, therefore one wonders what could be expected from the new ICT technologies, as for example Automatic Vehicle Location, License Plate Recognition, detection of mobile devices and so on. A simulation experiment is proposed prior to deploy the technology for a pilot project. The simulation emulates the logging and time stamping of a sample of equipped vehicles providing real-time estimates of travel times for the whole population of vehicles and OD pattern of the equipped vehicles are considered real-time estimates of the dynamic OD pattern for the whole population of vehicles. The main objective of this paper is to explore the quality of the data produced by the Bluetooth and Wi-Fi detection of mobile devices equipping vehicles to estimate time dependent OD matrices. Ad hoc procedures based on Kalman Filtering have been designed and implemented successfully and the numerical results of the computational experiments are presented and discussed.Postprint (published version

Feasibility of expanding traffic monitoring systems with floating car data technology

Trajectory information reported by certain vehicles (Floating Car Data or FCD) can be applied to monitor the road network. Policy makers face difficulties when deciding to invest in the expansion of their infrastructure based on inductive loops and cameras, or to invest in a FCD system. This paper targets this decision. The provided FCD functionality is investigated, minimum requirements are determined and reliability issues are researched. The communication cost is derived and combined with other elements to assess the total costs for different scenarios. The outcome is to target a penetration rate of 1%, a sample interval of 10 seconds and a transmission interval of 30 seconds. Such a deployment can accurately determine the locations of incidents and traffic jams. It can also estimate travel times accurately for highways, for urban roads this is limited to a binary categorization into normal or congested traffic. No reliability issues are expected. The most cost efficient scenario when deploying a new FCD system is to launch a smartphone application. For Belgium, this costs 13 million EUR for 10 years. However, it is estimated that purchasing data from companies already acquiring FCD data through their own product could reduce costs with a factor 10

A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. In addition to traffic counts, the present contribution takes advantage of probe trajectories, whose capture is made possible by new measurement technologies. It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the information about the flow distribution on links and containing inherently the ODM assignment. Further, an original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimisation problem, where the functional to be minimized consists of five convex functions, each modelling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having close origins and destinations, positivity of traffic flows. A primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations

Roadway System Assessment Using Bluetooth-Based Automatic Vehicle Identification Travel Time Data

This monograph is an exposition of several practice-ready methodologies for automatic vehicle identification (AVI) data collection systems. This includes considerations in the physical setup of the collection system as well as the interpretation of the data. An extended discussion is provided, with examples, demonstrating data techniques for converting the raw data into more concise metrics and views. Examples of statistical before-after tests are also provided. A series of case studies were presented that focus on various real-world applications, including the impact of winter weather on freeway operations, the economic benefit of traffic signal retiming, and the estimation of origin-destination matrices from travel time data. The technology used in this report is Bluetooth MAC address matching, but the concepts are extendible to other AVI data sources

Investigation of the Use of Bluetooth Sensors for Travel Time Studies under Indian Conditions

Travel time and its reliability are becoming increasingly important for a variety of real time applications such as Advanced Traveller Information Systems (ATIS), Route Guidance Systems (RGS) etc. which are a part of the Intelligent Transport Systems (ITS). They assist the road users in making timely decisions regarding their trip such as departure time, mode choice, route choice etc. There are various sensors which capture and monitor the travel time across a study corridor. However, their performance under Indian conditions is a major concern due to the vast heterogeneity in vehicle classes and the lack of lane discipline. Data from the GPS units equipped in the public transport buses are the current source of reliable travel time information. But these buses account for only 1% of the total traffic and have an inherent modal bias. One such technology which can capture data from across the vehicle classes and from any roadway of interest is the Bluetooth sensor. This study reports on the use of a Bluetooth based sensor to capture the travel time data and evaluate the reliability along two alternate routes in Chennai, India. Results from the cumulative frequency diagrams (CFD\u27s) which are used to evaluate the reliability are also compared against the various reliability index measures