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The artificial neural network (ANN) approach has been recognized as a capable technique to model the highly complex and nonlinear problem of travel time prediction. In addition to the nonlinearity, a traffic system is also temporally and spatially dynamic. Addressing the temporal-spatial relationships of a traffic system in the context of neural networks, however, has not received much attention. Furthermore, many of the past studies have not fully explored the inclusion of incident information into the ANN model development, despite that incident might be a major source of prediction degradations. Additionally, directly deriving corridor travel times in a one-step manner raises some intractable problems, such as pairing input-target data, which have not yet been adequately discussed. In this study, the corridor travel time prediction problem has been divided into two stages with the first stage on prediction of the segment travel time and the second stage on corridor travel time aggregation methodologies of the predicted segmental results. To address the dynamic nature of traffic system that are often under the influence of incidents, time delay neural network (TDNN), state-space neural network (SSNN), and an extended state-space neural network (ExtSSNN) that incorporates incident inputs are evaluated for travel time prediction along with a traditional back propagation neural network (BP) and compared with baseline methods based on historical data. In the first stage, the empirical results show that the SSNN and ExtSSNN, which are both trained with Bayesian regulated Levenberg Marquardt algorithm, outperform other models. It is also concluded that the incident information is redundant to the travel time prediction problem with speed and volume data as inputs. In the second stage, the evaluations on the applications of the SSNN model to predict snapshot travel times and experienced travel times are made. The outcomes of these evaluations are satisfactory and the method is found to be practically significant in that it (1) explicitly reconstructs the temporalspatial traffic dynamics in the model, (2) is extendable to arbitrary O-D pairs without complete retraining of the model, and (3) can be used to predict both traveler experiences and system overall conditions.

City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods. / <p>QC 20190531</p>

It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections. The main idea behind this methodology is that average speed on a section of roadway is constant unless a shockwave is created due to change in flow rate or density of traffic. In the proposed methodology first the road section is discretized into a number of smaller road segments and the average speed of each segment is calculated based on the available information obtained from probe vehicles during the current time interval. If a new shockwave is detected, the average speed of the road segment is adjusted to account for the change in the traffic conditions. In order to detect shockwaves, first, a two phase piecewise linear regression is used to find the points at which a vehicle has changed its speed. Then, the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified using a data filtering procedure and a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is applied to find propagation speed and spatial and temporal extent of each shockwave. The performance of this methodology was tested using one simulated signalized intersection, trajectories obtained from video processing of a section of freeway in California, and trajectories obtained from two freeway sections in Ontario. The results of this thesis show that the proposed methodology is able to detect shockwaves and predict travel time even with a small sample of vehicles. These results show that traffic data acquisition systems which are based on anonymously tracking of vehicles are a viable substitution to the tradition traffic data collection systems especially in relatively rural areas.

It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections. The main idea behind this methodology is that average speed on a section of roadway is constant unless a shockwave is created due to change in flow rate or density of traffic. In the proposed methodology first the road section is discretized into a number of smaller road segments and the average speed of each segment is calculated based on the available information obtained from probe vehicles during the current time interval. If a new shockwave is detected, the average speed of the road segment is adjusted to account for the change in the traffic conditions. In order to detect shockwaves, first, a two phase piecewise linear regression is used to find the points at which a vehicle has changed its speed. Then, the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified using a data filtering procedure and a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is applied to find propagation speed and spatial and temporal extent of each shockwave. The performance of this methodology was tested using one simulated signalized intersection, trajectories obtained from video processing of a section of freeway in California, and trajectories obtained from two freeway sections in Ontario. The results of this thesis show that the proposed methodology is able to detect shockwaves and predict travel time even with a small sample of vehicles. These results show that traffic data acquisition systems which are based on anonymously tracking of vehicles are a viable substitution to the tradition traffic data collection systems especially in relatively rural areas.

Urban areas are currently facing challenges in terms of traffic congestion due to city expansion and population increase. In some cases, physical solutions are limited. For example, in certain areas it is not possible to expand roads or build a new bridge. Therefore, making public transpiration (PT) affordable, more attractive and intelligent could be a potential solution for these challenges. Accuracy in bus running time and bus arrival time is a key component of making PT attractive to ridership. In this thesis, a dynamic model based on Kalman filter (KF) has been developed to predict bus running time and dwell time while taking into account real-time road incidents. The model uses historical data collected by Automatic Vehicle Location system (AVL) and Automatic Passenger Counters (APC) system. To predict the bus travel time, the model has two components of running time prediction (long and short distance prediction) and dwell time prediction. When the bus closes its doors before leaving a bus stop, the model predicts the travel time to all downstream bus stops. This is long distance prediction. The model will then update the prediction between the bus’s current position and the upcoming bus stop based on real-time data from AVL. This is short distance prediction. Also, the model predicts the dwell time at each coming bus stop. As a result, the model reduces the difference between the predicted arrival time and the actual arrival time and provides a better understanding for the transit network which allows lead to have a good traffic management.

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Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Travel time estimation can be used in strategical distribution of ambulances and ambulance stations. A more accurate travel time estimation can lead to a better distribution of these ambulance sites. External factors such as weather and traffic conditions can affect the travel time from a starting location to a destination. In this work, we investigate how the SOS Alarm dataset of ambulance trips data and the machine learning model Gradient Boosted Decision Trees can be used to estimate travel time, and how these estimationscan be improved by incorporating aforementioned conditions when predicting travel time. Results showed that reasonable performance can be achieved for a subset of data where the precise origin and destination is known compared to a subset where the precise origin is unknown, and that traffic conditions could improve model performance on a subset of data containing trips only for a single route. Including weather represented as individual weather parameters did not, however, lead to enhanced performance.

With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. ?? Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. ?? A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. ?? Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results.