Short-Term Travel Time Prediction on Freeways

Short-term travel time prediction supports the implementation of proactive traffic management and control strategies to alleviate if not prevent congestion and enable rational route choices and traffic mode selections to enhance travel mobility and safety. Over the last decade, Bluetooth technology has been increasingly used in collecting travel time data due to the technology’s advantages over conventional detection techniques in terms of direct travel time measurement, anonymous detection, and cost-effectiveness. However, similar to many other Automatic Vehicle Identification (AVI) technologies, Bluetooth technology has some limitations in measuring travel time information including 1) Bluetooth technology cannot associate travel time measurements with different traffic streams or facilities, therefore, the facility-specific travel time information is not directly available from Bluetooth measurements; 2) Bluetooth travel time measurements are influenced by measurement lag, because the travel time associated with vehicles that have not reached the downstream Bluetooth detector location cannot be taken at the instant of analysis. Freeway sections may include multiple distinct traffic stream (i.e., facilities) moving in the same direction of travel under a number of scenarios including: (1) a freeway section that contain both a High Occupancy Vehicle (HOV) or High Occupancy Toll (HOT) lane and several general purpose lanes (GPL); (2) a freeway section with a nearby parallel service roadway; (3) a freeway section in which there exist physically separated lanes (e.g. express versus collector lanes); or (4) a freeway section in which a fraction of the lanes are used by vehicles to access an off ramp. In this research, two different methods were proposed in estimating facility-specific travel times from Bluetooth measurements. Method 1 applies the Anderson-Darling test in matching the distribution of real-time Bluetooth travel time measurements with reference measurements. Method 2 first clusters the travel time measurements using the K-means algorithm, and then associates the clusters with facilities using traffic flow model. The performances of these two proposed methods have been evaluated against a Benchmark method using simulation data. A sensitivity analysis was also performed to understand the impacts of traffic conditions on the performance of different models. Based on the results, Method 2 is recommended when the physical barriers or law enforcement prevent drivers from freely switching between the underlying facilities; however, when the roadway functions as a self-correcting system allowing vehicles to freely switching between underlying facilities, the Benchmark method, which assumes one facility always operating faster than the other facility, is recommended for application. The Bluetooth travel time measurement lag leads to delayed detection of traffic condition variations and travel time changes, especially during congestion and transition periods or when consecutive Bluetooth detectors are placed far apart. In order to alleviate the travel time measurement lag, this research proposed to use non-lagged Bluetooth measurements (e.g., the number of repetitive detections for each vehicle and the time a vehicle spent in the detection zone) for inferring traffic stream states in the vicinity of the Bluetooth detectors. Two model structures including the analytical model and the statistical model have been proposed to estimate the traffic conditions based on non-lagged Bluetooth measurements. The results showed that the proposed RUSBoost classification tree achieved over 94% overall accuracy in predicting traffic conditions as congested or uncongested. When modeling traffic conditions as three traffic states (i.e., the free-flow state, the transition state, and the congested state) using the RUSBoost classification tree, the overall accuracy was 67.2%; however, the accuracy in predicting the congested traffic state was improved from 84.7% of the two state model to 87.7%. Because traffic state information enables the travel time prediction model to more timely detect the changes in traffic conditions, both the two-state model and the three-state model have been evaluated in developing travel time prediction models in this research. The Random Forest model was the main algorithm adopted in training travel time prediction models using both travel time measurements and inferred traffic states. Using historical Bluetooth data as inputs, the model results proved that the inclusion of traffic states information consistently lead to better travel time prediction results in terms of lower root mean square errors (improved by over 11%), lower 90th percentile absolute relative error ARE (improved by over 12%), and lower standard deviations of ARE (improved by over 15%) compared to other model structures without traffic states as inputs. In addition, the impact of traffic state inclusion on travel time prediction accuracy as a function of Bluetooth detector spacing was also examined using simulation data. The results showed that the segment length of 4~8 km is optimal in terms of the improvement from using traffic state information in travel time prediction models

Methodologies for Estimating Traffic Flow on Freeways Using Probe Vehicle Trajectory Data

Probe vehicle data are increasingly becoming the primary source of traffic data. As probe vehicle data become more widespread, it is imperative that methods are developed so that traffic state estimators such as flow, density, and speed can be derived from such data. In this dissertation three different methodologies are proposed for predicting traffic flow or volume on a freeway. All of the proposed methodologies exploit several different traffic flow theories in conjunction with probe vehicle data to predict traffic flow. The first methodology takes advantage of the fundamental diagram or speed-flow relationship. The relationship states that flow can be estimated when speed is known. In this case, flow is traffic volume and speed comes from probe vehicles. Flow is predicted for four different models of fundamental diagrams and is analyzed at different time aggregation intervals. Results show that of the four fundamental diagrams, Van Aerde’s Model is the best performing model with the lowest average percent error. It is also observed that flow prediction is more accurate during low speed (congestion) compared to high speed (free-flow) conditions. The second methodology exploits the shockwave theory, which pertains to the propagation of a change (discontinuity) in traffic flow. From probe vehicle trajectories, shockwave is estimated as the boundary between free-flow and congested regimes of traffic flow. After clustering the traffic regimes into free-flow and congested periods, the traffic flow during congestion is estimated using the Northwestern congested-regime fundamental diagram. From this estimation, the flow during free-flow is then predicted. Analyses show that the percent error of the predicted flow during free-flow ranges from -9 to 1%. The third methodology is the car-following approach which relies on the spacing or distance between a leader and follower which can be directly measured from the trajectories. Based on a set of known probability distributions, the position of the follower vehicle with respect to the lead vehicle is estimated given that the spacing between the two random probe vehicles is known. A framework is developed to automatically process probe trajectories to extract relevant probe data under stop-and-go traffic conditions. The model is tested based on NGSIM datasets. The results show that when vehicle spacing is small the prediction of follower position is very accurate. As spacing increases the error in predicted follower position also increases. Though there exists some estimation error, all three approaches can reasonably predict flow for freeways using probe vehicle data

A deep machine learning approach for predicting freeway work zone delay using big data

The introduction of deep learning and big data analytics may significantly elevate the performance of traffic speed prediction. Work zones become one of the most critical factors causing congestion impact, which reduces the mobility as well as traffic safety. A comprehensive literature review on existing work zone delay prediction models (i.e., parametric, simulation and non-parametric models) is conducted in this research. The research shows the limitations of each model. Moreover, most previous modeling approaches did not consider user delay for connected freeways when predicting traffic speed under work zone conditions. This research proposes Deep Artificial Neural Network (Deep ANN) and Convolution Neural Network (CNN) traffic speed prediction models, for upstream freeway segments, including those on connected freeways, under work zone conditions. The developed models are able to identify the congestion on the connected links in addition to the upstream mainline segments. The models predict traffic speed with work zone conditions based on traffic volume approaching the work zone, speed during normal conditions, work zone capacity, distance from work zone, vertical road gradient, downstream traffic volume and type of freeway segment. Moreover, the previous efforts in non-parametric approaches did not consider a solution to the overfitting problem of Artificial Neural Network (ANN). The proposed Deep ANN and CNN models use a dropout regularization to mitigate the overfitting issues. When comparing the CNN model to the Deep ANN model and the results of the Work Zone Interactive Management APplication-Planning (WIMAP-P) model, the testing results show higher accuracy with the CNN model compared to the other two models. The CNN model has filters that extract useful inputs from previous layers and reduces the overfitting problems. Dropout regularization technique is used to prevent the co-adaptation of training data. The CNN model is calibrated by varying the number of neurons at each hidden layer, the number of hidden layers, the optimizer algorithm, the filter height and the filter stride. The results indicate that the CNN model outperforms Deep ANN and the model of WIMAP-P in predicting traffic speed under work zone conditions. While traditional efforts were conducted previously on predicting traffic congestion on the upstream freeway segments, the developed CNN model helps transportation agencies in planning for work zones by including both connected freeways and the upstream segments when predicting traffic speed under work zone conditions. Therefore, transportation agencies can prepare more accurate congestion mitigation plans, and provide more accurate user delay plans

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

Optimization of path based sensor spacing on a freeway segment for travel time prediction during incidents

Congestion on freeways is increasing and a key source of it is non-recurring incidents. Accurate vehicle travel time predictions are needed during these incidents in order for roadway users to make informed trip decisions. Path based sensors are becoming a leading technology in gathering real-time travel time data. The data is used to make travel time predictions that are then provided through various means, such as dynamic message signs, to roadway users. These types of sensor are located at stationary points along a roadway and collect individual vehicle travel time data from vehicles as they drive pass the sensors. The accuracy of the predictions, in terms of representing future travel times, is dependent on many factors including the sensor spacing along the roadway, the duration and location of a traffic incident, and the uncongested and congested traffic speeds and traffic flows. Understanding the relationship between the travel time prediction accuracy and the different variables is necessary to optimize sensor spacing. In addition, because incidents occur at different times of the day, have varying durations, occur at different locations, and cause different capacity reductions depending on the severity of the incident, the sensor spacing cannot be based on one incident scenario. Instead, multiple incident scenarios, along with the probability of each occurring, needs to be taken into account. Path based sensor spacing during incidents on a freeway segment is optimized in this dissertation. In addition, the marginal benefit of additional sensors is calculated. A mathematical model and a solution methodology are developed. The mathematical model applies macroscopic traffic principles and shock wave theory. It calculates the travel time prediction error by sensor spacing during an incident on a freeway segment. The solution algorithm consists of four main steps. First, historical incident data for the roadway are gathered. Second, the mathematical model is applied to determine the average travel time prediction error by sensor spacing for each of the historical incidents. Third, the weighted average travel time prediction error by sensor spacing is calculated, which considers all the possible incidents and the frequency of each occurring. Fourth, the optimal spacing is chosen which minimizes the weighted average error. The applicability of the model and solution methodology is demonstrated through a case study of a ten mile freeway segment in Northern New Jersey

Work Zone Simulator Analysis: Driver Performance and Acceptance of Alternate Merge Sign Configurations

Improving work zone road safety is an issue of great interest due to the high number of crashes observed in work zones. Departments of Transportation (DOTs) use a variety of methods to inform drivers of upcoming work zones. One method used by DOTs is work zone signage configuration. It is necessary to evaluate the efficiency of different configurations, by law, before implementation of new signage designs that deviate from national standards. This research presents a driving simulator based study, funded by the Missouri Department of Transportation (MoDOT) that evaluates a driver’s response to work zone sign configurations. This study has compared the Conventional Lane Merge (CLM) configurations against MoDOT’s alternate configurations. Study participants within target populations, chosen to represent a range of Missouri drivers, have attempted four work zone configurations, as part of a driving simulator experience. The test scenarios simulated both right and left work zone lane closures for both the CLM and MoDOT alternatives. Travel time was measured against demographic characteristics of test driver populations. Statistical data analysis was used to investigate the effectiveness of different configurations employed in the study. The results of this study were compared to results from a previous MoDOT to compare result of field and simulation study about MoDOT’s alternate configurations

Development of dynamic recursive models for freeway travel time predictions

Traffic congestion has been a major problem in metropolitan areas, which is caused by either insufficient roadway capacity or unforesceable incidents. In order to promote the efficiency of the existing roadway networks and mitigate the impact of traffic congestion, the development of a sound prediction model for travel times is desirable. A comprehensive literature review about existing prediction models was conducted by investigating the advantages, disadvantages, and limitations of each model. Based on the features and properties of previous models, the base models including exponential smoothing model (ESM), moving average model (MAM), and Kalman filtering model (KFM) are developed to capture stochastic properties of traffic behavior for travel time prediction. By incorporating KFM into ESM and MAM, three dynamic recursive prediction models including dynamic exponential smoothing model (DESM), improved dynamic exponential smoothing model (JDESM), and dynamic moving average model (DMAM) are developed, in which the time-varying weight parameters are optimized based on the most recent observation. Model evaluation has been conducted to analyze prediction accuracy under various traffic conditions (e.g., free-flow condition, recurrent and non-recurrent congested traffic conditions). Results show that the IDESM in general outperforms other models developed in this study in prediction accuracy and stability. In addition, the feature and logic of the IDESM lead to its high transferability and adaptability, which could enable the prediction model to perform well at multiple locations and deal with complicated traffic conditions. Besides the proficient capability, the IDESM is easy to implement in the real world transportation network. Thus, the IDESM is proven an appealing approach for short-time travel time prediction under various traffic conditions. The application scope of the IDESM is identified, while the optimal prediction intervals are also suggested in this study

New Framework and Decision Support Tool to Warrant Detour Operations During Freeway Corridor Incident Management

As reported in the literature, the mobility and reliability of the highway systems in the United States have been significantly undermined by traffic delays on freeway corridors due to non-recurrent traffic congestion. Many of those delays are caused by the reduced capacity and overwhelming demand on critical metropolitan corridors coupled with long incident durations. In most scenarios, if proper detour strategies could be implemented in time, motorists could circumvent the congested segments by detouring through parallel arterials, which will significantly improve the mobility of all vehicles in the corridor system. Nevertheless, prior to implementation of any detour strategy, traffic managers need a set of well-justified warrants, as implementing detour operations usually demand substantial amount of resources and manpower. To contend with the aforementioned issues, this study is focused on developing a new multi-criteria framework along with an advanced and computation-friendly tool for traffic managers to decide whether or not and when to implement corridor detour operations. The expected contributions of this study are: * Proposing a well-calibrated corridor simulation network and a comprehensive set of experimental scenarios to take into account many potential affecting factors on traffic manager\u27s decision making process and ensure the effectiveness of the proposed detour warrant tool; * Developing detour decision models, including a two-choice model and a multi-choice model, based on generated optima detour traffic flow rates for each scenario from a diversion control model to allow responsible traffic managers to make best detour decisions during real-time incident management; and * Estimating the resulting benefits for comparison with the operational costs using the output from the diversion control model to further validate the developed detour decision model from the overall societal perspective

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (LiDAR) Data

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources. Nevertheless, using current emerging technologies such as Light Detection and Ranging (LiDAR) data, it may be possible to predict commodity type from truck body types or trailers. For example, refrigerated trailers are commonly used to transport perishable produce and meat products, tank trailers are for fuel and other liquid products, and specialized trailers carry livestock. The main goal of this research is to develop methods using side-fired LiDAR data to distinguish between specific types of truck trailers beyond what is generally possible with traditional vehicle classification sensors (e.g., piezoelectric sensors and inductive loop detectors). A multi-array LiDAR sensor enables the construction of 3D-profiles of vehicles since it measures the distance to the object reflecting its emitted light. In this research 16-beam LiDAR sensor data are processed to estimate vehicle speed and extract useful information and features to classify semi-trailer trucks hauling ten different types of trailers: a reefer and non-reefer dry van, 20 ft and 40 ft intermodal containers, a 40 ft reefer intermodal container, platforms, tanks, car transporters, open-top van/dump and aggregated other types (i.e., livestock, logging, etc.). In addition to truck-trailer classification, methods are developed to detect empty and loaded platform semi-trailers. K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), and Support Vector Machines (SVM) supervised machine learning algorithms are implemented on the field data collected on a freeway segment that includes over seven-thousand trucks. The results show that different trailer body types and empty and loaded platform semi-trailers can be classified with a very high level of accuracy ranging from 85% to 98% and 99%, respectively. To enhance the accuracy by which multiple LiDAR frames belonging to the same truck are merged, a new algorithm is developed to estimate the speed while the truck is within the field of view of the sensor. This algorithm is based on tracking tires and utilizes line detection concepts from image processing. The proposed algorithm improves the results and allows creating more accurate 2D and 3D truck profiles as documented in this thesis