Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors

A practical system is described for the real-time estimation of travel time across an arterial segment with multiple intersections. The system relies on matching vehicle signatures from wireless sensors. The sensors provide a noisy magnetic signature of a vehicle and the precise time when it crosses the sensors. A match (re-identification) of signatures at two locations gives the corresponding travel time of the vehicle. The travel times for all matched vehicles yield the travel time distribution. Matching results can be processed to provide other important arterial performance measures including capacity, volume/capacity ratio, queue lengths, and number of vehicles in the link. The matching algorithm is based on a statistical model of the signatures. The statistical model itself is estimated from the data, and does not require measurement of ‘ground truth’. The procedure does not require measurements of signal settings; in fact, signal settings can be inferred from the matched vehicle results. The procedure is tested on a 1.5 km (0.9 mile)-long segment of San Pablo Avenue in Albany, CA, under different traffic conditions. The segment is divided into three links: one link spans four intersections, and two links each span one intersection.     

Length-based vehicle classification using event-based loop detector data

Length-based vehicle classification is an important topic in traffic engineering, because estimation of traffic speed from single loop detectors usually requires the knowledge of vehicle length. In this paper, we present an algorithm that can classify vehicles passing by a loop detector into two categories: long vehicles and regular cars. The proposed algorithm takes advantage of event-based loop detector data that contains every vehicle detector actuation and de-actuation “event”, therefore time gaps between consecutive vehicles and detector occupation time for each vehicle can be easily derived. The proposed algorithm is based on an intuitive observation that, for a vehicle platoon, longer vehicles in the platoon will have relatively longer detector occupation time. Therefore, we can identify longer vehicles by examining the changes of occupation time in a vehicle platoon. The method was tested using the event-based data collected from Trunk Highway 55 in Minnesota, which is a high speed arterial corridor controlled by semi-actuated coordinated traffic signals. The result shows that the proposed method can correctly classify most of the vehicles passing by a single loop detector.     

Vehicle reidentification and travel time measurement across freeway junctions using the existing detector infrastructure

Conventional vehicle detectors are capable of monitoring discrete points along the freeway but do not provide information about conditions on the link between detectors. Knowledge of conditions on the link is useful to operating agencies for enabling timely decisions in response to various delay causing events and hence to reduce the resulting congestion of the freeway system. This paper presents an approach that matches vehicle measurements between detector stations to provide information on the conditions over the link between the detectors rather than relying strictly on the aggregate point measurements from the detectors. In particular this work reidentifies measurements from distinct vehicles using the existing loop detector infrastructure. Here the distinct vehicles are the long vehicles, but depending on the vehicle population or type of detector used, one might chose to use some other reproducible feature.This new methodology represents an important advancement over preceding loop based vehicle reidentification, as illustrated herein, it enables vehicle reidentification across a major diverge and a major merge. The examples include a case where the reidentification algorithm responded to delay between two detector stations an hour before the delay was locally observable at either of the stations used for reidentification. While previous loop based reidentification work was limited to dual loop detectors, the present effort also extends the methodology to single loop detectors; thereby making it more widely applicable. Although the research uses loop detector data, the algorithm would be equally applicable to data obtained from many other traffic detectors that provide reproducible vehicle features.     

Mixed manual/semi-automated traffic: a macroscopic analysis

The use of advanced technologies and intelligence in vehicles and infrastructure could make the current highway transportation system much more efficient. Semi-automated vehicles with the capability of automatically following a vehicle in front as long as it is in the same lane and in the vicinity of the forward looking ranging sensor are expected to be deployed in the near future. Their penetration into the current manual traffic will give rise to mixed manual/semi-automated traffic. In this paper, we analyze the fundamental flow–density curve for mixed traffic using flow–density curves for 100% manual and 100% semi-automated traffic. Assuming that semi-automated vehicles use a time headway smaller than today’s manual traffic average due to the use of sensors and actuators, we have shown using the flow–density diagram that the traffic flow rate will increase in mixed traffic. We have also shown that the flow–density curve for mixed traffic is restricted between the flow–density curves for 100% manual and 100% semi-automated traffic. We have presented in a graphical way that the presence of semi-automated vehicles in mixed traffic propagates a shock wave faster than in manual traffic. We have demonstrated that the presence of semi-automated vehicles does not change the total travel time of vehicles in mixed traffic. Though we observed that with 50% semi-automated vehicles a vehicle travels 10.6% more distance than a vehicle in manual traffic for the same time horizon and starting at approximately the same position, this increase is marginal and is within the modeling error. Lastly, we have shown that when shock waves on the highway produce stop-and-go traffic, the average delay experienced by vehicles at standstill is lower in mixed traffic than in manual traffic, while the average number of vehicles at standstill remains unchanged.     

Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach

This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell’s method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark’s approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.     

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.     

A recursive, cell processing model for predicting freeway travel times

Travel time information influences driver behaviour and can contribute to reducing congestion and improving network efficiency. Consequently many road authorities disseminate travel time information on road side signs, web sites and radio traffic broadcasts. Operational systems commonly rely on speed data obtained from inductive loop detectors and estimate travel times using simple algorithms that are known to provide poor predictions particularly on either side of the peak period. This paper presents a new macroscopic model for predicting freeway travel times which overcomes the limitations of operational ‘instantaneous’ speed models by drawing on queuing theory to model the processing of vehicles in sections or cells of the freeway. The model draws on real-time speed, flow and occupancy data and is formulated to accommodate varying geometric conditions, the relative distribution of vehicles along the freeway, variations in speed limits, the impact of ramp flows and fixed or transient bottlenecks. Field validation of the new algorithm was undertaken using data from two operational freeways in Melbourne, Australia. Consistent with the results of simulation testing, the validation confirmed that the recursive model provided a substantial improvement in travel time predictions when compared to the model currently used to provide real-time travel time information to motorists in Melbourne.     

Identifying the onset of congestion rapidly with existing traffic detectors

From an operations standpoint the most important function of a traffic surveillance system is determining reliably whether the facility is free flowing or congested. The second most important function is responding rapidly when the facility becomes congested. These functions are complicated by the fact that conventional vehicle detectors are only capable of monitoring discrete points along the roadway while incidents may occur at any location on the facility. The point detectors are typically placed at least one-third of a mile apart and conditions between the detectors must be inferred from the local measurements.This paper presents a new approach for traffic surveillance that addresses these issues. It uses existing dual loop detector stations to match vehicle measurements between stations and monitor the entire roadway. Rather than expending a considerable effort to detect congested conditions, the research employs a relatively simple strategy to look for free flow traffic. Whenever a unique vehicle passes the downstream station, the algorithm looks to see if a similar vehicle passed the upstream station within a time window that is bounded by feasible travel times. The approach provides vehicle reidentification and travel time measurement on freeways during free flow and through the onset of congestion. If desired, other algorithms can be used with the same detectors to provide similar measurements during congested conditions. The work should prove beneficial for traffic management and traveler information applications, while promising to be deployable in the short term.     

Vehicle sorting for platoon formation: Impacts on highway entry and throughput

Automated highway systems (AHS) are intended to increase the throughput and safety of roadways through computer control, communication and sensing. In the “platoon” concept for AHS, vehicles travel on highways in closely spaced groups. To maximize benefits, it is desirable to form platoons that are reasonably large (five or more vehicles), and it is also desirable to ensure that platoons remain intact for considerable distances. This paper develops and evaluates strategies for organizing vehicles into platoons at highway entrances, with the objective of maximizing the distance that platoons stay intact, so that they do not need to be regrouped into new platoons on the highway itself. Fundamentally, this entails grouping vehicles according to their destination. We evaluate various strategies in which vehicles are sorted on entrance ramps, with respect to platoon sizes, throughput and platoon formation time.     

Using high-resolution event-based data for traffic modeling and control: An overview

Research on using high-resolution event-based data for traffic modeling and control is still at early stage. In this paper, we provide a comprehensive overview on what has been achieved and also think ahead on what can be achieved in the future. It is our opinion that using high-resolution event data, instead of conventional aggregate data, could bring significant improvements to current research and practices in traffic engineering. Event data records the times when a vehicle arrives at and departs from a vehicle detector. From that, individual vehicle’s on-detector-time and time gap between two consecutive vehicles can be derived. Such detailed information is of great importance for traffic modeling and control. As reviewed in this paper, current research has demonstrated that event data are extremely helpful in the fields of detector error diagnosis, vehicle classification, freeway travel time estimation, arterial performance measure, signal control optimization, traffic safety, traffic flow theory, and environmental studies. In addition, the cost of event data collection is low compared to other data collection techniques since event data can be directly collected from existing controller cabinet without any changes on the infrastructure, and can be continuously collected in 24/7 mode. This brings many research opportunities as suggested in the paper.     

Estimating travel times and vehicle trajectories on freeways using dual loop detectors

Recent research has investigated various means of measuring link travel times on freeways. This search has been motivated in part by the fact that travel time is considered to be more informative to users than local velocity measurements at a detector station. But direct travel time measurement requires the correlation of vehicle observations at multiple locations, which in turn requires new communications infrastructure and/or new detector hardware. This paper presents a method for estimating link travel time using data from an individual dual loop detector, without requiring any new hardware. The estimation technique exploits basic traffic flow theory to extrapolate local conditions to an extended link. In the process of estimating travel times, the algorithm also estimates vehicle trajectories. The work demonstrates that the travel time estimates are very good provided there are no sources of delay, such as an incident, within a link.     

Noise prediction for highways in Thailand

A model of highway traffic noise is formulated based on vehicle types. The data were collected from local highways in Thailand with free-flow traffic conditions. First, data on vehicle noise was collected from individual vehicles using sound level meters placed at a reference distance. Simultaneously, measurements were made of vehicles’ spot speeds. Secondly, are data for building the highway traffic noise model. This consists of traffic noise levels, traffic volumes by vehicle classification, average spot speeds by vehicle type, and the geometric dimension of highway sections. The free-flow traffic noise model is generated from this database. A reference energy mean emission level (the basic noise) level for each type of vehicles is developed based on direct measurement of Leq (10 s) from the real running condition of each type of vehicles. Modification of terms and parameters are used to make the model fit highway traffic characteristics and different types of vehicle.     

Improved vehicle classification from dual-loop detectors in congested traffic

Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Length-based vehicle classification from dual loop detectors is among the lowest cost technologies commonly used for collecting these data. Like many vehicle classification technologies, the dual loop approach works well in free flow traffic. Effective vehicle lengths are measured from the quotient of the detector dwell time and vehicle traversal time between the paired loops. This approach implicitly assumes that vehicle acceleration is negligible, but unfortunately at low speeds this assumption is invalid and length-based classification performance degrades in congestion.To addresses this problem, we seek a solution that relies strictly on the measured effective vehicle length and measured speed. We analytically evaluate the feasible range of true effective vehicle lengths that could underlie a given combination of measured effective vehicle length, measured speed, and unobserved acceleration at a dual loop detector. From this analysis we find that there are small uncertainty zones where the measured length class can differ from the true length class, depending on the unobserved acceleration. In other words, a given combination of measured speed and measured effective vehicle length falling in the uncertainty zones could arise from vehicles with different true length classes. Outside of the uncertainty zones, any error in the measured effective vehicle length due to acceleration will not lead to an error in the measured length class. Thus, by mapping these uncertainty zones, most vehicles can be accurately sorted to a single length class, while the few vehicles that fall within the uncertainty zones are assigned to two or more classes. We find that these uncertainty zones remain small down to about 10 mph and then grow exponentially as speeds drop further.Using empirical data from stop-and-go traffic at a well-tuned loop detector station the best conventional approach does surprisingly well; however, our new approach does even better, reducing the classification error rate due to acceleration by at least a factor of four relative to the best conventional method. Meanwhile, our approach still assigns over 98% of the vehicles to a single class.     

Optimal speed detector density for the network with travel time information

In the expressway network, detectors are installed on the links for detecting the travel time information while the predicted travel time can be provided by the route guidance system (RGS). The speed detector density can be determined to influence flow distributions in such a way that the precision of the travel time information and the social cost of the speed detectors are optimized, provided that each driver chooses the minimum perceived travel time path in response to the predicted travel time information. In this paper, a bilevel programming model is proposed for the network with travel time information provided by the RGS. The lower-level problem is a probit-based traffic assignment model, while the upper-level problem is to determine the speed detector density that minimizes the measured travel time error variance as well as the social cost of the speed detectors. The sensitivity analysis based algorithm is proposed for the bilevel programming problem. Numerical examples are provided to illustrate the applications of the proposed model and of the solution algorithm.     

Speed estimation and length based vehicle classification from freeway single-loop detectors

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector’s measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.     

A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials

Estimation of time-dependent arterial travel time is a challenging task because of the interrupted nature of urban traffic flows. Many research efforts have been devoted to this topic, but their successes are limited and most of them can only be used for offline purposes due to the limited availability of traffic data from signalized intersections. In this paper, we describe a real-time arterial data collection and archival system developed at the University of Minnesota, followed by an innovative algorithm for time-dependent arterial travel time estimation using the archived traffic data. The data collection system simultaneously collects high-resolution “event-based” traffic data including every vehicle actuations over loop detector and every signal phase changes from multiple intersections. Using the “event-based” data, we estimate time-dependent travel time along an arterial by tracing a virtual probe vehicle. At each time step, the virtual probe has three possible maneuvers: acceleration, deceleration and no-speed-change. The maneuver decision is determined by its own status and surrounding traffic conditions, which can be estimated based on the availability of traffic data at intersections. An interesting property of the proposed model is that travel time estimation errors can be self-corrected, because the trajectory differences between a virtual probe vehicle and a real one can be reduced when both vehicles meet a red signal phase and/or a vehicle queue. Field studies at a 11-intersection arterial corridor along France Avenue in Minneapolis, MN, demonstrate that the proposed model can generate accurate time-dependent travel times under various traffic conditions.     

Empirical flow-density and speed-spacing relationships: Evidence of vehicle length dependency

Traffic flow theory has come to a point where conventional, fixed time averaged data are limiting our insight into critical behavior both at the macroscopic and microscopic scales. This paper develops a methodology to measure relationships of density and vehicle spacing on freeways. These relationships are central to most traffic flow theories but have historically been difficult to measure empirically. The work leads to macroscopic flow-density and microscopic speed-spacing relationships in the congested regime derived entirely from dual loop detector data and then verified against the NGSIM data set. The methodology eliminates the need to seek out stationary conditions and yields clean relationships that do not depend on prior assumptions of the curve shape before fitting the data. Upon review of the clean empirical relationships a key finding of this work is the fact that many of the critical parameters of the macroscopic flow-density and microscopic speed-spacing relationships depend on vehicle length, e.g., upstream moving waves should travel through long vehicles faster than through short vehicles. Thus, the commonly used assumption of a homogeneous vehicle fleet likely obscures these important phenomena. More broadly, if waves travel faster or slower depending on the length of the vehicles through which the waves pass, then the way traffic is modeled should be updated to explicitly account for inhomogeneous vehicle lengths.     

Arterial traffic signal optimization: A person-based approach

This paper presents a real-time signal control system that optimizes signal settings based on minimization of person delay on arterials. The system’s underlying mixed integer linear program minimizes person delay by explicitly accounting for the passenger occupancy of autos and transit vehicles. This way it can provide signal priority to transit vehicles in an efficient way even when they travel in conflicting directions. Furthermore, it recognizes the importance of schedule adherence for reliable transit operations and accounts for it by assigning an additional weighting factor on transit delays. This introduces another criterion for resolving the issue of assigning priority to conflicting transit routes. At the same time, the system maintains auto vehicle progression by introducing the appropriate delays associated with interruptions of platoons. In addition to the fact that it utilizes readily available technologies to obtain the inputs for the optimization, the system’s feasibility in real-world settings is enhanced by its low computation time. The proposed signal control system is tested on a four-intersection segment of San Pablo Avenue arterial located in Berkeley, California. The findings show the system’s capability to outperform pretimed (i.e., fixed-time) optimal signal settings by reducing total person delay. They have also demonstrated its success in reducing bus person delay by efficiently providing priority to transit vehicles even when they travel in conflicting directions.     

A real-time traffic signal control system: architecture, algorithms, and analysis

The paper discusses a real-time traffic-adaptive signal control system referred to as RHODES. The system takes as input detector data for real-time measurement of traffic flow, and “optimally” controls the flow through the network. The system utilizes a control architecture that (1) decomposes the traffic control problem into several subproblems that are interconnected in an hierarchical fashion, (2) predicts traffic flows at appropriate resolution levels (individual vehicles and platoons) to enable pro-active control, (3) allows various optimization modules for solving the hierarchical subproblems, and (4) utilizes a data structure and computer/communication approaches that allow for fast solution of the subproblems, so that each decision can be downloaded in the field appropriately within the given rolling time horizon of the corresponding subproblem. The RHODES architecture, algorithms, and its analysis are presented. Laboratory test results, based on implementation of RHODES on simulation models of actual scenarios, illustrate the effectiveness of the system.     

A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection

The advancements in communication and sensing technologies can be exploited to assist the drivers in making better decisions. In this paper, we consider the design of a real-time cooperative eco-driving strategy for a group of vehicles with mixed automated vehicles (AVs) and human-driven vehicles (HVs). The lead vehicles in the platoon can receive the signal phase and timing information via vehicle-to-infrastructure (V2I) communication and the traffic states of both the preceding vehicle and current platoon via vehicle-to-vehicle (V2V) communication. We propose a receding horizon model predictive control (MPC) method to minimise the fuel consumption for platoons and drive the platoons to pass the intersection on a green phase. The method is then extended to dynamic platoon splitting and merging rules for cooperation among AVs and HVs in response to the high variation in urban traffic flow. Extensive simulation tests are also conducted to demonstrate the performance of the model in various conditions in the mixed traffic flow and different penetration rates of AVs. Our model shows that the cooperation between AVs and HVs can further smooth out the trajectory of the latter and reduce the fuel consumption of the entire traffic system, especially for the low penetration of AVs. It is noteworthy that the proposed model does not compromise the traffic efficiency and the driving comfort while achieving the eco-driving strategy.