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Traffic congestion and the resulting stop-and-go motion of traffic on freeways increases travel delay, consumes more fuel and poses greater crash risks to motorists. Variable speed limit (VSL) control, where motorists are advised to travel under a lower speed limit on certain sections of the road, has been used to smooth traffic flow and reduce congestion. This research brief summarizes findings from the project which developed VSL control strategies to reduce fuel consumption and emissions on a freeway with multiple bottlenecks. The core objective was to minimize fuel use and emissions with no or minimal increase of travel time.

This paper investigates the accuracy of mean density estimation from direct sensing at link and network levels. Different calculation methods are compared depending on sensor type, probe vehicles or loop detectors, and availability to quantify the magnitude of expected errors. Probe data are essential to reduce the error but accurate density estimation requires high penetration rates, which is hardly true in practice. We enhance the fishing rate method, i.e. using the ratio of probes detected at the loop locations over the loop flow, to estimate density. Accurate density estimation at the link level can only be obtained when probes and loop data are available in real-time. At the network level, accurate density estimations can be obtained when combining loop and probe observations, even if few links capture both data sources. It requires applying the proper analytical formulation to aggregate the local observations, i.e. carefully defining fishing rates at this scale. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network.

This paper develops a novel anticipatory Variable Speed Limit (VSL) control strategy that incorporates driver behavior based on trajectory data from probe vehicles. In particular, the research examines how the speed limit controls can be coordinated and optimized to reduce lane changing and overall braking, thus to achieve greater traffic throughput, safety, and sustainability. Driver behavior, such as acceleration/deceleration and lane changing, as reflected in individual trajectory data from GPS enabled probe vehicles are crucial for early detection of shockwave formation and proactive selection of speed limits; and consequently delay, or even eliminate breakdown formation. The core of this approach is the incorporation of a lane changing model which provides a more robust integrated speed limit selection and shockwave detection framework than the one we have developed in an earlier paper. The control principle is formulated in a generic fashion that finds the optimal speed limit control variables for either separate or simultaneous reduction of travel time, crash rates, and fuel consumption over a prediction horizon. The findings from the paper suggested that the VSL strategy was able to result in fewer lane changing rate (LCR) compared to the No-VSL case. Also, the developed algorithm was able to produce higher frequency of lower acceleration and deceleration rates than the No-VSL case, which indicated a smoother acceleration and deceleration pattern that corresponded to lower emission and fuel consumption. The performance of the algorithm was also examined under different probe penetration rates and congestion levels to identify the % of probe needed to achieve simultaneous mobility, safety, and environmental objectives. [ABSTRACT FROM AUTHOR]

The macroscopic fundamental diagram (MFD) is a graphical method used to characterize the traffic state in a road network and to monitor and evaluate the effect of traffic management. For the determination of an MFD, both traffic volumes and traffic densities are needed. This study introduces a methodology to determine an MFD using combined data from probe vehicles and loop detector counts. The probe vehicles in this study were taxis with GPS. The ratio of taxis in the total traffic was determined and used to convert taxi density to the density of all vehicles. This ratio changes over the day and between different links. We found evidence that the MFD was rather similar for days in the same year based on real data collected in Changsha, China. The difference between MFDs made of data from 2013 and 2015 reveals that the modification of traffic control can influence the MFD significantly. A macroscopic fundamental diagram could also be drawn for an area with incomplete data gained from a sample of loop detectors. An MFD based on incomplete data can also be used to monitor the emergence and disappearance of congestion, just as an MFD based on complete traffic data.

• We develop an integer programming model with a set of novel constraints to estimate the queue profile at a signalized intersection by exploiting the spatiotemporal propagation of shockwaves; • Unlike existing studies that use triangles or polygons to approximate queue profiles, our model allows us to detect queue profiles of any shape as long as they conform to the propagation of shockwaves; • Our model can categorize cycles into different types and utilize data in cycles of the same type, which helps to construct the queue profile; and • Numerical experiments using simulated data and real data demonstrate that our model can produce satisfactory results even when the penetration rate is as low as 10–20% and the sampling interval is as high as 20–30 seconds. Queues at signalized intersections bring interruptions to the smooth movement of vehicles and slow down the traffic in urban road networks. Although queue length estimation has attracted much attention in the literature, recent studies indicate increasing interest in queue profile estimation, which is crucial to many extensive analysis. In this research, we propose an innovative approach to estimating the queue profile at a signalized intersection by exploiting the spatiotemporal propagation of shockwaves. The input to our model includes locations and speeds of probe vehicles on a signalized link and the starting time of red in signal cycles. The model then outputs the corresponding queue profile. We first classify data points of probe vehicles into moving and stopped states. We then develop an integer programming model with a set of novel constraints to estimate the queue profile, which conforms to the spatiotemporal propagation of shockwaves. Unlike existing studies that use triangles or polygons to approximate queue profiles, our model allows us to detect queue profiles of any shape. Our model can also categorize cycles into different types and utilize data in cycles of the same type, which helps to construct the queue profile. We validate our model using both simulated and real data. Results show that our model is capable of producing satisfactory results even when the penetration rate is as low as 10–20% and the sampling interval is as high as 20–30 seconds. [ABSTRACT FROM AUTHOR]

Queue length is a crucial measure of intersection performance. Probe vehicles (PVs) with advanced sensors are capable of recording vehicle trajectories that can be used to estimate queue length, a technique of which has received considerable attention in the past decade. Noticeably, this technique usually requires high PV penetration rates (e.g., above 25%) in order to ensure estimation accuracy. Though the PVs are expected to increase, their penetration rate will still remain relatively low in the near future. Meanwhile, the initial queue length is another important factor that directly relates to queue dynamics at each cycle. However, most of the studies failed to adequately account for the effect of the initial queue on cyclic queue length estimation. To address the above challenges, this paper proposes a cycle-based end of queue estimation method using sampled vehicle trajectory data under relatively low penetration rates. Two major steps are involved: first, vehicle arrival process is modeled as a certain distribution in line with traffic conditions and an expectation maximum (EM) procedure is employed to estimate the arrival rate of each cycle; then, both ends of the queue and initial queue are estimated at each cycle based on shockwave theory. Microscopic traffic simulator VISSIM is utilized to examine the performance of the method. The experimental results reveal that the cycle-based end of the queue can be estimated with desirable accuracy in different scenarios, e.g., undersaturated, oversaturated, and queue spillback conditions. The comparison with the state-of-the-art methods further helps to verify the advantage of the method, especially under low-penetration-rate conditions. [ABSTRACT FROM AUTHOR]

The primary aim of this study is to support the policy decisions on selection of a work zone layout that would have least deterrence to the traffic flow on busy urban arterials carrying heterogeneous traffic. The traffic flow characteristics were recorded with two cameras and the speed variation was obtained by plying probe vehicles fitted with Global Positioning Systems (GPSs) repeatedly in the section. The traffic stream speed and capacity of the various types of work zone sections were analyzed and it is found that near side lane closure has a relatively least effect on capacity (15%) whereas the effect of run around type closure is maximum (46%). The reasons for the variations in speed and capacity at these types of work zones were examined and reported. Besides, the application of the study results with respect to planning of work zone layout is discussed. [ABSTRACT FROM AUTHOR]

Monitoring the occupancy of on-street parking spaces on a city-wide scale is still an open issue. Past research demonstrated the viability of parking crowd-sensing by means of the standard on-board sensors of probe vehicles, foreseeing the use of high-mileage vehicles, like taxis. Nevertheless, the achievable spatio-temporal sensing coverage has never been deeply investigated. In this paper, we investigate the suitability of taxi fleets of different sizes to crowd-sense on-street parking availability. We considered 579 road segments in San Francisco (USA), covered both by sensors of the SFpark project and by the GPS traces of 536 taxis. For each of these segments, we computed the taxi transit frequencies, representing the achievable coverage by vehicles equipped with sensors detecting empty parking spots. By combining these frequencies with parking occupancy data coming from SFpark, we estimated the potential quality of crowd-sensed on-street parking information for different fleet sizes. Moreover, we investigated the impact of different misdetection amounts, and Kalman filters to handle them. The results show that a total of 300 taxis can crowd-sense on-street parking availability with an error of up to ±1 stall in 86% of the cases. Moreover, the quality of the sensors is as important as the fleet size (300 taxis with 10% probability of misreadings provide availability information comparable to 486 taxis with 16% probability), while the use of Kalman filters did not lead to statistically significant improvements. In conclusion, the traffic management authorities should consider parking crowd-sensing via probe vehicles as a promising alternative to the expensive deployment of the static parking sensors. [ABSTRACT FROM AUTHOR]

The fundamental principle of intelligent transportation systems is to match the complexity of travel demands with advanced supply-side analysis, evaluation, management and control strategies. A fundamental limitation is the lack of basic knowledge of travel demands at the network level. Modeling and sensor technology is primarily limited to aggregate parameters or micro-simulations based on aggregate distributions of behavior. Global positioning systems (GPS) are one of several available technologies that allow individual vehicle trajectories to be recorded and analyzed. Potential applications of GPS are implementation in probe vehicles to deliver real-time performance data to complement loop and other sensor data and implementation in vehicles from sampled households to record route choice behavior. A flexible GPS-based data collection unit has been designed which incorporates GPS, data logging capabilities, two-way wireless communications and a user interface in an embedded system that eliminates (or minimizes) driver interaction. This paper describes the design and initial implementation tests of this unit.

The introduction of electronic toll collection on the eight bridges crossing San Francisco Bay has provided the means for a relatively simple and low cost system for measuring travel times on many Bay Area bridges and roads. The toll tags sued for electronic toll collection can be read by readers at various locations on congested roads. The time of reading is recorded so that the time difference between when a vehicle passes one reader and passes the next can be computed. Such a system is already operating in Houston, where it is the primary source of travel time data. Capital costs per reader site where such systems have been implemented range from $18,000-$38,000 and for the operations center from $37,000-$86,000. Annual operating costs range from $4,000-$6,000 per detector site and $48,000-$96,000 for the operations center. The Bay Area bridges and their approaches are prime candidates for such a system. Most of the congested freeways and a few arterials near the Bay are also good candidates. The extent of the area for which toll tags would provide satisfactory travel time estimates will depend on how many vehicles choose to use electronic toll collection. This, in turn, will depend on Caltrans policies on tolls and the number of lanes available for toll tags and cash payment. Key words: Vehicle probes Travel time measurement Toll tags

The Los Angeles County Metropolitan Transportation Authority/California Department of Transportation/California Highway Patrol Freeway Service Patrol (FSP) program is the largest in the nation, operating 144 service vehicles on 40 beats covering393 center-line freeway miles in Los Angeles County. The Caltrans District 7 Transportation Management Center (TMC) exercises FSP fleet control via the California Highway Patrol Computer Aided Dispatch (CAD) system. Each freeway service patrol truck is equipped with a Mobile Data Terminal (MDT), polled by the Automatic Vehicle Location (AVL) system.  The system includes a Transportation Management Solutions Incorporated (TMSI, now Orbital TMSI) Geo-Position System (GPS) that can identify transponder locations to within 100 feet.  There is potential for using the GPS and/or the AVL information to determine FSP truck speeds automatically because field units are polled frequently, and GPS locations are sufficiently accurate. This research assesses the feasibility of using existing FSP trucks as probe vehicles for measuring level of service on Los Angeles freeways.  If the information FSP trucks provide in Los Angeles is of sufficient quality and quantity to measure level of service on the network, then FSP trucks (or other similarly-equipped fleets) would also be useful for measuring LOS in other Caltrans Districts, especially those with relatively fewer loop detectors than Caltrans District 7.

This paper reports on the first phase of the location technology evaluation for probe vehicles. Two technologies were evaluated, Global Positioning Systems (GPS) and the cellular phone tracking technology developed by US Wireless. Although GPS has shown great potential for vehicle probes, much of the previous research is theoretical in nature. Very little work has been done in the areas of experimental research, implementation or deployment. Most of the field tests were anecdotal; a systematic approach is highly desired to develop a vehicle probe system that is reliable and efficient for traffic management. If GPS is widely deployed in cellular phones, as GTE in 1998 predicted would happen, GPS technology will become even more attractive and realistic for vehicle probe activities. A custom software package was developed as part of this project in order to conduct the technology evaluation. The software, the Travel Information Probe System (TIPS) maps positions of probes of arbitrary accuracy to an embedded Geographical Information System (GIS) in order to determine the path the probe took. Once the path has been determined, the software calculates the travel time for each road segment traversed. The preliminary analysis of two Bay Area counties showed that accurate location technologies are capable of producing travel time information for nearly all roads. A technology with 20-meter accuracy can produce data for 99.2% of road segments and 98.9% of the freeway segments in the two counties studied.

• A novel Bayesian approach is proposed for estimating queue lengths at signalized intersections. • High-frequency probe vehicle trajectory data are used. • Both the queue length and the discharging shockwave speed are modelled stochastically. • An efficient expectation maximum algorithm is developed. • Estimates are accurate and robust even under low penetration of probe vehicles. A novel Bayesian approach is proposed for estimating the maximum queue lengths of vehicles at signalized intersections using high-frequency trajectory data of probe vehicles. The queue length estimates are obtained from a distribution estimated over several neighboring cycles via a maximum a posteriori method. An expectation maximum algorithm is proposed for efficiently solving the estimation problem. Through a battery of simulation experiments and a real-world case study, the proposed approach is shown to produce more accurate and robust estimates than two benchmark estimation methods. Fairly good accuracy is achieved even when the probe vehicle penetration rate is 2%. [ABSTRACT FROM AUTHOR]

Methods of pavement roughness characterisations using connected vehicles are poised to scale beyond the frequency, span and affordability of existing methods that require specially instrumented vehicles and skilled technicians. However, speed variability and differences in suspension behaviour require segmentation of the connected vehicle data to achieve some level of desired precision and accuracy with relatively few measurements. This study evaluates the reliability of a Road Impact Factor (RIF) transform under stop-and-go conditions. A RIF-transform converts inertial signals from on-board accelerometers and speed sensors to roughness indices (RIF-indices), in real-time. The case studies collected data from 18 different buses during their normal operation in a small urban city. Within 30 measurements, the RIF-indices distributed normally with an average margin-of-error below 6%. This result indicates that a large number of measurements will provide a reliable estimate of the average roughness experienced. Statistical t-tests distinguished the relatively small differences in average roughness levels among the roadway segments evaluated. In conclusion, when averaging roughness measurements from the same type of vehicle moving at non-uniform speeds, the RIF-transform will provide ever-increasing precision and accuracy as the traversal volume increases. [ABSTRACT FROM AUTHOR]

• This study proposed a method using measurements from a vehicle on the opposite lane. • This method has the advantage in quickly responding unexpected incidences. • We could estimate the time period of an accident and the capacity value. This study proposes a method that estimates traffic states using measurements from a vehicle running on the opposite lane in addition to probe vehicle data and examine the sensitivity of the estimates in relation to variabilities of the input data and measurements. A number of studies on traffic state estimation fusing several sensing data have been reported. Most of the studies use data from traffic detectors installed at fixed locations and data from moving objects such as probe vehicles. Traffic detectors provide valuable volume information of all running vehicles which cannot be observed from sample moving objects. However, in local areas in Japan as well as in Asian cities, detector installations are very much limited like one in every 10 to 15 km on a motorway in our country. This study therefore attempts to utilize measurements from a vehicle running on the opposite lane instead of detector measurements, since a vehicle on the opposite lane running backward can in principle measure counts of passing vehicles running forward. In this study, we employ the variational theory to estimate the traffic states utilizing the count measurement from the opposite lane in addition to probe vehicle data on the forward direction and examine the sensitivity of the estimates in relation to variabilities of the input data and measurements. The validation finds that the proposed method can estimate traffic states more accurately than one using only probe vehicle data. Especially, this method has the advantage in quickly responding unexpected incidences such as accidents and vehicle malfunctions. [ABSTRACT FROM AUTHOR]

Researchers previously demonstrated that a roughness index called the road impact factor (RIF) is directly proportional to the international roughness index (IRI) when measured under identical conditions. A RIF-transform converts inertial signals from connected vehicle accelerometers and speed sensors to produce RIF-indices in real time. This research examines the relative sensitivities of the RIF and the IRI to variations in dominant profile wavelengths. The findings are that both indices characterise roughness from spatial wavelengths up to 2 m with equal sensitivity. However, the RIF-transform maintains its sensitivity when characterising roughness from wavelengths beyond that. The case studies used a certified inertial profiler to collect both RIF and IRI data simultaneously from five different pavement surface types. The RIF/IRI proportionality factors distributed normally among the profiles tested. This result affirms that the RIF and IRI generally agrees. However, differences in the dominant profile wavelength among pavements will produce some spread in the degree of roughness that the indices express. [ABSTRACT FROM AUTHOR]

Network screening is a key element in identifying and prioritizing hazardous sites for engineering treatment. Traditional screening methods have used observed crash frequency or severity ranking criteria and statistical modelling approaches, despite the fact that crash-based methods are reactive. Alternatively, surrogate safety measures (SSMs) have become popular, making use of new data sources including video and, more rarely, GPS data. The purpose of this study is to examine vehicle manoeuvres of braking and accelerating extracted from a large quantity of GPS data collected using the smartphones of regular drivers, and to explore their potential as SSMs through correlation with historical collision frequency and severity across different facility types. GPS travel data was collected in Quebec City, Canada in 2014. The sample for this study contained over 4000 drivers and 21,000 trips. Hard braking (HBEs) and accelerating events (HAEs) were extracted and compared to historical crash data using Spearman’s correlation coefficient and pairwise Kolmogorov-Smirnov tests. Both manoeuvres were shown to be positively correlated with crash frequency at the link and intersection levels, though correlations were much stronger when considering intersections. Locations with more braking and accelerating also tend to have more collisions. Concerning severity, higher numbers of vehicle manoeuvres were also related to increased collision severity, though this relationship was not always statistically significant. The inclusion of severity testing, which is an independent dimension of safety, represents a substantial contribution to the existing literature. Future work will focus on developing a network screening model that incorporates these SSMs. [ABSTRACT FROM AUTHOR]

The international roughness index (IRI) is the prevalent indicator used to assess and forecast road maintenance needs. The fixed parameters of its simulation model provide the advantage of requiring relatively few traversals to produce a consistent index. However, the static parameters also cause the model to under-represent roughness that riders experience from profile wavelengths outside of the model’s response range. A connected vehicle method that uses a similar but different index to characterise roughness can do so by accounting for all vibration wavelengths that the actual vehicles experience. This study characterises and compares the precision of each method. The field studies indicate that within seven traversals, the connected vehicle approach could achieve the same level of precision as the procedure used to produce the IRI. For a given vehicle and segment lengths longer than 50 m, the margin-of-error diminished below 1.5% after 50 traversals, and continued to improve further as the traversal volume grew. Practitioners developing new tools to evaluate pavement performance will benefit from this study by understanding the precision trade-off to recommend the best practices in utilising the connected vehicle method. [ABSTRACT FROM AUTHOR]

Emerging sensing technologies such as probe vehicles equipped with Global Positioning System (GPS) devices on board provide us real-time vehicle trajectories. They are helpful for the understanding of the cases that are significant but difficult to observe because of the infrequency, such as gridlock networks. On the premise of this type of emerging technology, this paper propose a sequential route choice model that describes route choice behavior, both in ordinary networks, where drivers acquire spatial knowledge of networks through their experiences, and in extraordinary networks, which are situations that drivers rarely experience, and applicable to real-time traffic simulations. In extraordinary networks, drivers do not have any experience or appropriate information. In such a context, drivers have little spatial knowledge of networks and choose routes based on dynamic decision making, which is sequential and somewhat forward-looking. In order to model these decision-making dynamics, we propose a discounted recursive logit model, which is a sequential route choice model with the discount factor of expected future utility. Through illustrative examples, we show that the discount factor reflects drivers’ decision-making dynamics, and myopic decisions can confound the network congestion level. We also estimate the parameters of the proposed model using a probe taxis’ trajectory data collected on March 4, 2011 and on March 11, 2011, when the Great East Japan Earthquake occurred in the Tokyo Metropolitan area. The results show that the discount factor has a lower value in gridlock networks than in ordinary networks. [ABSTRACT FROM AUTHOR]

Mobile communication instruments have made detecting traffic incidents possible by using floating traffic data. This paper studies the properties of traffic flow dynamics during incidents and proposes incident detection methods using floating data collected by probe vehicles equipped with on-board global positioning system (GPS) equipment. The proposed algorithms predict the time and location of traffic congestion caused by an incident. The detection rate and false rate of the models are examined using a traffic flow simulator, and the performance measures of the proposed methods are compared with those of previous methods. [ABSTRACT FROM AUTHOR]

The routing and scheduling for trucks and vans in an urban road network depends critically on the state of the road network. Trucks and vans impose significant costs on other road users and the environment, so improved routing and scheduling benefits more than just the logistics industry. However, small and medium size enterprises (SMEs) in the logistics business cannot justify investment in planning systems. In this paper, an autonomous routing and scheduling system which is available to SMEs is proposed and the efficiency of the system is investigated. The proposed system accumulates vehicle location data in a central server and uses it to generate traffic information. Test simulations using a grid network demonstrate the effects of utilizing and sharing vehicle location data on delivery efficiency. The simulation results show that the improvement of delivery efficiency is mainly due to the reduction of penalty cost for early and late arrival at the customer location. It is also shown that the system leads to the buffer effect from variations in traffic conditions on delivery cost and this effect is enhanced by taking travel time uncertainty into consideration. It is further shown that the presence of measurement periods with insufficient data results in unreliable routing and scheduling. For a reliable system, data collection over a wider area is required rather than dense data in a subset of links. [ABSTRACT FROM PUBLISHER]

Well-defined relationships between flow and density averaged spatially across urban traffic networks, more commonly known as Macroscopic Fundamental Diagrams (MFDs), have been recently verified to exist in reality. Researchers have proposed using MFDs to monitor the status of urban traffic networks and to inform the design of network-wide traffic control strategies. However, it is also well known that empirical MFDs are not easy to estimate in practice due to difficulties in obtaining the requisite data needed to construct them. Recent works have devised ways to estimate a network’s MFD using limited trajectory data that can be obtained from GPS-equipped mobile probe vehicles. These methods assume that the market penetration level of mobile probe vehicles is uniform across the entire set of OD pairs in the network; however, in reality the probe vehicle market penetration rate varies regionally within a network. When this variation is combined with the imbalance of probe trip lengths and travel times, the compound effects will further complicate the estimation of the MFD. To overcome this deficit, we propose a method to estimate a network’s MFD using mobile probe data when the market penetration rates are not necessarily the same across an entire network. This method relies on the determination of appropriate average probe penetration rates, which are weighted harmonic means using individual probe vehicle travel times and distances as the weights. The accuracy of this method is tested using synthetic data generated in the INTEGRATION micro-simulation environment by comparing the estimated MFDs to the ground truth MFD obtained using a 100% market penetration of probe vehicles. The results show that the weighted harmonic mean probe penetration rates outperform simple (arithmetic) average probe penetration rates, as expected. This especially holds true as the imbalance of demand and penetration level increases. Furthermore, as the probe penetration rates are generally not known, an algorithm to estimate the probe penetration rates of regional OD pairs is proposed. This algorithm links count data from sporadic fixed detectors in the network to information from probe vehicles that pass the detectors. The simulation results indicate that the proposed algorithm is very effective. Since the data needed to apply this algorithm are readily available and easy to collect, the proposed algorithm is practically feasible and offers a better approach for the estimation of the MFD using mobile probe data, which are becoming increasingly available in urban environments. [ABSTRACT FROM AUTHOR]

This paper describes a crowdsourcing-based system for phase and timing estimation of pre-timed traffic signals. The input crowd is a real-time feed of sparse and low-frequency probe vehicle data, and the output is an estimated collection of Signal Phase and Timing (SPaT) information. The estimations could be ultimately fed into a connected vehicle's driver assistant application. Different from the authors' previous work, the approach described in this paper ensures the accuracy of the SPaT estimations even in the presence of queues. This was achieved by investigating the probe data influenced by the heavy traffic and the delay in queues. This paper is also a sequel to the authors' previous work as it provides an in-depth overview of the crowdsourcing algorithms and their back-end implementation. The accuracy of the crowdsourcing algorithm is also experimentally evaluated for a selection of pre-timed traffic lights in San Francisco, CA, USA, by utilizing a real-time data feed of San Francisco's public buses as an example data source. [ABSTRACT FROM PUBLISHER]

In many cities, taxis equipped with global positioning system (GPS) devices are used as probe vehicles. However, there are cases when the data from such taxis are not suitable for use as traffic information because of long data polling intervals. There are also developing countries in which taxis are not equipped with GPS devices. In such cases, a probe vehicle system has to be established by allocating probe vehicle devices to taxi dispatch centers (TDCs). In this study, the authors analyze the efficient allocation of such probe vehicle devices to TDCs under the cost constraint and the assumption of independent link travel times, adopting a definition of efficiency based on the loss to travel time information users resulting from information errors. The travel time information obtained using a probe vehicle system is the sample mean of a statistical distribution and should be treated as a statistical variable. Thus, the information error, which leads to losses to information users, is also a statistical variable. The allocation problem minimizes such losses. The results from a case study show that the loss to information users changes with the time of day and among different city areas. It is also shown that probe devices should be evenly allocated among TDCs. If either the number of probe devices or the data collection period is limited, more probe devices should be allocated to the central urban area where the traffic is heavier. [ABSTRACT FROM PUBLISHER]

This article proposes a batch-mode algorithm to handle the large databases generated from experimentations using probe vehicles. This algorithm can locate raw Global Positioning System (GPS) positions on a map, but can also be used to correct map-matching errors introduced by real time map-matching algorithms. For each journey, the algorithm globally searches for the closest path to the GPS positions, and so is inspired from the “path to path” algorithm's family. It uses the Multiple Hypothesis Technique (MHT) and relies on an innovative weighting system based on the area between the GPS points and the arcs making up the path. For high performance, the algorithm uses an iterative program and the data is stored in tree form. [ABSTRACT FROM AUTHOR]

The objective of this paper is to demonstrate the feasibility of estimating traffic signal phase and timing from statistical patterns in low-frequency vehicular probe data. We use a public feed of bus location and velocity data in the city of San Francisco, CA, USA, as an example data source. We show that it is possible to estimate, fairly accurately, cycle times and the duration of reds for fixed-time traffic lights traversed by buses using a few days' worth of aggregated bus data. Furthermore, we also estimate the start of greens in real time by monitoring the movement of buses across intersections. The results are encouraging, given that each bus sends an update only sporadically ( $\approx$ every 200 m) and that bus passages are infrequent (every 5–10 min) . When made available on an open server, such information about the traffic signals' phase and timing can be valuable in enabling new fuel efficiency and safety functionalities in connected vehicles. Velocity advisory systems can use the estimated timing plan to calculate velocity trajectories that reduce idling time at red signals and therefore improve fuel efficiency and lower emissions. Advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red. Intersection collision avoidance and active safety systems could also benefit from the prediction. [ABSTRACT FROM PUBLISHER]

Traffic estimation is crucial to a number of tasks such as traffic management and road engineering. We propose an approach for metropolitan-scale traffic estimation with probe vehicles that periodically send location and speed updates to a monitoring center. In our approach, we use the flow speed on a road link within a time slot to indicate the traffic condition of the road segment at the given time slot, which is approximated by the average value of probe speeds. By analyzing a large data set of two-year probe data collected from a fleet of around 4,000 taxis in Shanghai, China, we find that a set of probe data may contain a lot of spatiotemporal vacancies over both time and space. This raises a serious missing data problem for road traffic estimation, which results from the naturally uneven distribution of probe vehicles over both time and space. Through empirical study based on the data set of real probe data using principal component analysis (PCA), we have observed that there are hidden structures within the traffic conditions of a road network. Inspired by this observation, we propose a compressive sensing-based algorithm for solving the missing data problem, which exploits the hidden structures for computing estimates for road traffic conditions. Different from existing approaches, our algorithm does not rely on complicated traffic models, which usually require costly training with field study and large data sets. With extensive experiments based on the data set of real probe data, we demonstrate that our proposed algorithm performs significantly better than other completing algorithms, including KNN and MSSA. Surprisingly, our algorithm can achieve an estimate error of as low as 20 percent even when more than 80 percent of probe data are missing. [ABSTRACT FROM AUTHOR]

Highlights: [•] Develops simple analytical estimation models for queue lengths from probe vehicles. [•] Derives probability distributions and moment generating functions for probe information types. [•] Gives steady-state and cycle-to-cycle estimation error formulas. [•] Compares the estimation errors of the models with VISSIM-microscopic simulation. [•] Shows how the errors change with primary parameters. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops two soft computing models, i.e., the multilayer feedforward network (MFN) based model and the adaptive-network-based fuzzy inference system (ANFIS) based model, to mine the traffic speed patterns/trends for a road link using the sparse historical probe vehicles (PVs) data at the same link. The two models and an additional naive arithmetical average model are tested on the field datasets obtained in some Beijing (China)'s urban expressways. The results illustrate that the soft computing based models have higher robustness to the problem of missing data and their generalization capabilities are better than the arithmetic average model. Comprehensively considering all the performance metrics suggest that the ANFIS offers the best model of traffic trends in studied links. Furthermore, the traffic trends produced by ANFIS provide us the opportunities to identify some meaningful hidden traffic speed patterns. The missing data's influence on the mined traffic speed patterns is also investigated. It is found that the reliability of mined traffic speed patterns decreases with the increasing of the missing data's percentage. Nevertheless, ANFIS based model shows great robustness to the missing data problem. [Copyright &y& Elsevier]

Traffic light sensing aims to detect the status of traffic lights which is valuable for many applications such as traffic management, traffic light optimization, and real-time vehicle navigation. In this work, we develop a system called POVA for traffic light sensing in large-scale urban areas. The system employs pervasive probe vehicles that just report real-time states of position and speed from time to time. POVA has advantages of wide coverage and low deployment cost. The important observation motivating the design of POVA is that a traffic light has a considerable impact on mobility of vehicles on the road attached to the traffic light. However, the system design faces three unique challenges: 1) Probe reports are by nature discrete while the goal of traffic light sensing is to determine the state of a traffic light at any time; 2) there may be a very limited number of probe reports in a given duration for traffic light state estimation; and 3) a traffic light may change its state with a variable interval. To tackle the challenges, we develop a new technique that makes the best use of limited probe reports as well as statistical features of light states. It first estimates the state of a traffic light at the time instant of a report by applying maximum a posterior estimation. Then, we formulate the state estimation of a light at any time into a joint optimization problem that is solved by an efficient heuristic algorithm. We have implemented the system and tested it with a fleet of around 4,000 probe taxis and 2,000 buses in Shanghai, China. Trace-driven experimentation and field study show that nearly 60 percent of traffic lights have an estimation error lower than 19 percent if 20,000 probe vehicles would be employed in the urban area of Shanghai. We further demonstrate that the estimation error rate is as low as 18 percent even when the number of available reports is merely 1 per minute. [ABSTRACT FROM AUTHOR]

Time headway distribution modeling is fundamental to many aspects of traffic flow studies such as capacity estimation, safety analysis, and microscopic simulation. Existing time headway distribution models have focused on the behavior of general vehicles. We examine the distribution of sampled vehicle headway (e.g., probes) on both single and multiple lane highway traffic streams. This study is divided into three parts: an empirical study, a simulation analysis, and an analytical derivation. The empirical study uses probe data obtained from Houston, Texas that was collected as part of the Automatic Vehicle Identification system of the Houston Transtar system. In the empirical study, a shifted negative exponential distribution was found to give the closest fit for both single and multiple lane cases. We found that if the volume level of the probes is low, regardless of the volume level of general vehicles, the headway of the probes followed a shifted negative exponential distribution. In the simulation study, we found that the time headway of probes does not necessarily follow the time headway distribution of general vehicles. Rather, it depends on many variables such as the volume level of general vehicles, the market penetration of probe vehicles, and the number of lanes. However, when the volume level of general vehicles is low, the headway of probes tends to follow the shifted negative exponential distribution at all levels of market penetration, together with the general vehicles. We analytically proved that if the time headway of general vehicles follows the shifted negative exponential distribution, then the time headway of the probes is the same as that of the general vehicles. [ABSTRACT FROM AUTHOR]

This paper describes the development of a systematic methodology for identifying and ranking bottlenecks using probe data collected by commercial global positioning system fleet management devices mounted on trucks. These data are processed in a geographic information system and assigned to a roadway network to provide performance measures for individual segments. The authors hypothesized that truck speed distributions on these segments can be represented by either a unimodal or bimodal probability density function and proposed a new reliability measure for evaluating roadway performance. Travel performance was classified into three categories: unreliable, reliably fast, and reliably slow. A mixture of two Gaussian distributions was identified as the best fit for the overall distribution of truck speed data. Roadway bottlenecks were ranked on the basis of both the reliability and congestion measurements. The method was used to evaluate the performance of Washington state roadway segments, and proved efficient at identifying and ranking truck bottlenecks. [ABSTRACT FROM AUTHOR]

Probe vehicles represent samples in a traffic stream, and hence, probe-based traffic monitoring relies on effective sampling of vehicles. Current research views sampling from a networkwide perspective, and a single sample size is specified for the entire network. Using this method, research has indicated that the smaller routes in a network, such as minor and other arterials, are not adequately monitored. Increasing the sample size is not always a feasible option because of the restrictions of cost for use of the wireless network bandwidth and the availability of required number of probe vehicles. In this article, the authors describe a research effort that identified and extended stratified sampling as a method for increasing the efficacy of probe-based traffic monitoring. A methodology for application of stratified sampling for probe-based traffic monitoring is developed and implemented using a heavily traveled suburban traffic network simulation as a case study. Also, the authors discuss results of the implementation and the future direction of research. [ABSTRACT FROM AUTHOR]

Abstract: In probe-based traffic monitoring systems, traffic conditions can be inferred based on the position data of a set of periodically polled probe vehicles. In such systems, the two consecutive polled positions do not necessarily correspond to the end points of individual links. Obtaining estimates of travel time at the individual link level requires the total traversal time (which is equal to the polling interval duration) be decomposed. This paper presents an algorithm for solving the problem of decomposing the traversal time to times taken to traverse individual road segments on the route. The proposed algorithm assumes minimal information about the network, namely network topography (i.e. links and nodes) and the free flow speed of each link. Unlike existing deterministic methods, the proposed solution algorithm defines a likelihood function that is maximized to solve for the most likely travel time for each road segment on the traversed route. The proposed scheme is evaluated using simulated data and compared to a benchmark deterministic method. The evaluation results suggest that the proposed method outperforms the bench mark method and on average improves the accuracy of the estimated link travel times by up to 90%. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to examine the performance of a new operational system for measuring traffic speeds and travel times which is based on information from a cellular phone service provider. Cellular measurements are compared with those obtained by dual magnetic loop detectors. The comparison uses data for a busy 14km freeway with 10 interchanges, in both directions, during January–March of 2005. The dataset contains 1284587 valid loop detector speed measurements and 440331 valid measurements from the cellular system, each measurement referring to a 5min interval. During one week in this period, 25 floating car measurements were conducted as additional comparison observations. The analyses include visual, graphical, and statistical techniques; focusing in particular on comparisons of speed patterns in the time–space domain. The main finding is that there is a good match between the two measurement methods, indicating that the cellular phone-based system can be useful for various practical applications such as advanced traveler information systems and evaluating system performance for modeling and planning. [Copyright &y& Elsevier]

The wide variety of remote sensors used in Intelligent Transportation Systems (ITS) applications (loops, probe vehicles, radar, cameras, etc.) has created a need for general methods by which data can be shared among agencies and users who own disparate computer systems. In this paper, we present a methodology that demonstrates that it is possible to create, encode, and decode a self-describing data stream using: 1) existing data description language standards; 2) parsers to enforce language compliance; 3) a simple content language that flows out of the data description language; and 4) architecture neutral encoders and decoders based on ASN.1. [ABSTRACT FROM PUBLISHER]

Aggressive driving is a key contributor to traffic incidents which deteriorate traffic flow, increase traffic congestion, and pose serious threats to driver and passenger safety. This paper presents a methodology for the estimation of driver aggressiveness and detection of traffic incidents using a game-theory based controller. We first present a game theory-based controlling mechanism, in which a witness vehicle (vehicle A) interacts with an aggressive vehicle (vehicle B) to estimate the aggressiveness of and to predict the future behavior of vehicle B. Second, we use a probe vehicle framework to detect incidents. Third, we apply shockwave theory to identify the location of the incident. Results show that the proposed method can estimate the aggressiveness of vehicle B with a high degree of accuracy. Numerical results obtained through simulation show that the proposed method obtains a better incident detection rate with more than 90% of the incidents detected, on average, with a nearly 91% classification rate and lower false alarm rate than three commonly used methods. It also requires less time to clear the traffic incident. The information obtained from the proposed system can be used to reduce traffic accidents caused by aggressive driving, thereby improving the safety of both drivers and passengers. [ABSTRACT FROM AUTHOR]

In this paper, we explore the consequences of using link travel time estimates with high variance to compute the minimum travel time route between an origin and destination pair. Because of platoon formation or for other reasons, vehicles on a link separated by small headways tend to have similar travel times. In other words, the covariance of link travel times of distinct vehicles which are close together may not be zero. It follows that the variance of the mean of travel times obtained from a sample of n vehicles on a same link over small time intervals is of the form a + b / n where a and b would usually be positive. This result has an important implication for the quality of road network travel time information given by Intelligent Transportation Systems (ITS)—that the variance of the estimate of mean travel time does not go to zero with increasing n . Thus the quality of information disseminated by ITS is not necessarily improved by increasing the market penetration of vehicles monitoring the system with the necessary equipment (termed probe vehicles). Estimates of a and b for a set of links are presented in the paper and consequences for probe-based ITS are explored by means of a simulation of such a system which is operational on an actual network. [ABSTRACT FROM AUTHOR]

This study analyzes measurements by backward moving observers that could be probe vehicles running backward on the opposite lane observing forward moving traffic to be investigated. These probe vehicles are called as backward probe vehicles (BP) and they are proven to measure the traffic flow and density. Using some advanced technology, a BP is assumed to estimate the flow of vehicles running forward from their passing time measurements along the BP trajectory. Then, as a useful application for the flow measurement by a BP, we propose a data assimilation method that estimates traffic states under an incident on an expressway section utilizing BP measurements in addition to conventional probe vehicles moving forward (forward probe vehicles) and detector data. Ample literature exists on traffic state estimation using several sensing data. However, they have difficulty in estimating traffic states during an incident, since the observations of the incident period and the declined flow rate due to the incident may not be sufficiently accurate. Therefore, this study proposes a state space model (SSM) that estimates traffic states under an incident on an expressway utilizing BP measurements. The model validation using a hypothetical network with an incident confirms the promising potential of the proposed model; that is, the reproducibility of traffic states using BP measurements is superior to one using forward probe measurements. [ABSTRACT FROM AUTHOR]