A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features

In the realm of Intelligent Transportation Systems (ITS), accurate traffic speed prediction plays an important role in traffic control and management. The study on the prediction of traffic speed has attracted considerable attention from many researchers in this field in the past three decades. In recent years, deep learning-based methods have demonstrated their competitiveness to the time series analysis which is an essential part of traffic prediction. These methods can efficiently capture the complex spatial dependency on road networks and non-linear traffic conditions. We have adopted the convolutional neural network-based deep learning approach to traffic speed prediction in our setting, based on its capability of handling multi-dimensional data efficiently. In practice,the traffic data may not be recorded with a regular interval, due to many factors, like power failure, transmission errors,etc.,that could have an impact on the data collection. Given that some part of our dataset contains a large amount of missing values, we study the effectiveness of a multi-view approach to imputing the missing values so that various prediction models can apply. Experimental results showed that the performance of the traffic speed prediction model improved significantly after imputing the missing values with a multi-view approach, where the missing ratio is up to 50%

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective

One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computational capacities have increased the use of machine learning (ML) to address traffic prediction, which is mostly modeled as a supervised regression problem. Although some studies have presented taxonomies to sort the literature in this field, they are mostly oriented to classify the ML methods applied and a little effort has been directed to categorize the traffic forecasting problems approached by them. As far as we know, there is no comprehensive taxonomy that classifies these problems from the point of view of both traffic and ML. In this paper, we propose a taxonomy to categorize the aforementioned problems from both traffic and a supervised regression learning perspective. The taxonomy aims at unifying and consolidating categorization criteria related to traffic and it introduces new criteria to classify the problems in terms of how they are modeled from a supervised regression approach. The traffic forecasting literature, from 2000 to 2019, is categorized using this taxonomy to illustrate its descriptive power. From this categorization, different remarks are discussed regarding the current gaps and trends in the addressed traffic forecasting area

The Prediction and Mitigation of Road Traffic Congestion Based on Machine Learning

Traffic congestion is a major issue for all developed countries. In most urbanised areas space is a scarce commodity. Therefore, better management of the existing roads to increase or maintain their capacity level is the only viable solution. Research in the last two decades has focused on Intelligent Transport Systems (ITS) development. Predicting traffic flow in real time can be used to prevent or alleviate future congestion. The key to an effective proactive method is a model that produces timely and accurate predictions. However, despite extensive research in this area, a reliable method is still not available. Therefore, in this thesis, we developed an accurate online road traffic flow prediction model, with a particular focus on heterogeneous traffic flow, for urbanised road networks. The contributions of this work include: Firstly, we conducted a comprehensive literature review and benchmark evaluation of existing machine learning models using a real dataset obtained from Transport for Greater Manchester. We investigated their prediction accuracy, time horizon sensitivity, and input feature settings (different classes of vehicles), to understand how they can affect their prediction accuracy. The experimental results show that the artificial neural network was the most successful at predicting short-term road traffic flow. Additionally, it was found that different classes of vehicles can improve prediction accuracy. Secondly, we examined three recurrent neural networks (a standard recurrent, a long short-term memory, and a gated recurrent unit). We compared their accuracy, training time, and sensitivity to architectural change using a new performance metric we developed to standardise the accuracy and training time into a comparable score (STATS). The experimental results show that the gated recurrent unit performed the best and was most stable against architectural changes. Conversely, the long short-term memory was the least stable model. Thirdly, we investigated different magnitudes of temporal patterns in the dataset, both short and long-term, to understand how contextual temporal data can improve prediction accuracy. We also developed a novel online dynamic temporal context neural network framework. The framework dynamically determines how useful a temporal data segment is for prediction, and weights it accordingly for use in the regression model. The experimental results show that short and long-term temporal patterns improved prediction accuracy. In addition, the proposed online dynamical framework improved prediction results by 10.8% when compared with a deep gated recurrent model. Finally, we investigated the dynamic nature of road traffic flow’s input features by examining their spatial and temporal relationships. We also developed a novel dynamic exogenous feature filter mechanism. The feature filter mechanism uses ’local windows’ to filter input features in real-time to improve prediction accuracy. The results show that a global correlation was insufficient to describe the complex and dynamic relationships between the input features. The local correlations (local windows) were able to identify additional geospatial and temporal relationships. Furthermore, the proposed feature filter mechanism was compared to a state-of-the-art method, a dynamic rolling window feature filter model. The experimental results showed that the proposed model was the most accurate, with an RMSE of 10.06%, closely followed by the dynamic rolling window feature filter model, with an RMSE of 10.98%. However, the proposed model was computationally much lighter than the rolling windows model

Operational research and simulation methods for autonomous ride-sourcing

Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems. The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method. The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage. The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces

Flow-Aware WPT k-Nearest Neighbours Regression for Short-Term Traffic Prediction

Robust and accurate traffic prediction is critical in modern intelligent transportation systems (ITS). One widely used method for short-term traffic prediction is k-nearest neighbours (kNN). However, choosing the right parameter values for kNN is problematic. Although many studies have investigated this problem, they did not consider all parameters of kNN at the same time. This paper aims to improve kNN prediction accuracy by tuning all parameters simultaneously concerning dynamic traffic characteristics. We propose weighted parameter tuples (WPT) to calculate weighted average dynamically according to flow rate. Comprehensive experiments are conducted on one-year real-world data. The results show that flow-aware WPT kNN performs better than manually tuned kNN as well as benchmark methods such as extreme gradient boosting (XGB) and seasonal autoregressive integrated moving average (SARIMA). Thus, it is recommended to use dynamic parameters regarding traffic flow and to consider all parameters at the same time