Survey of ETA prediction methods in public transport networks

The majority of public transport vehicles are fitted with Automatic Vehicle Location (AVL) systems generating a continuous stream of data. The availability of this data has led to a substantial body of literature addressing the development of algorithms to predict Estimated Times of Arrival (ETA). Here research literature reporting the development of ETA prediction systems specific to busses is reviewed to give an overview of the state of the art. Generally, reviews in this area categorise publications according to the type of algorithm used, which does not allow an objective comparison. Therefore this survey will categorise the reviewed publications according to the input data used to develop the algorithm. The review highlighted inconsistencies in reporting standards of the literature. The inconsistencies were found in the varying measurements of accuracy preventing any comparison and the frequent omission of a benchmark algorithm. Furthermore, some publications were lacking in overall quality. Due to these highlighted issues, any objective comparison of prediction accuracies is impossible. The bus ETA research field therefore requires a universal set of standards to ensure the quality of reported algorithms. This could be achieved by using benchmark datasets or algorithms and ensuring the publication of any code developed

BusTr: Predicting Bus Travel Times from Real-Time Traffic

We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided. We demonstrate that our neural sequence model improves over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE) and training stability. We also demonstrate significant generalization gains over simpler models, evaluated on longitudinal data to cope with a constantly evolving world.Comment: 14 pages, 2 figures, 5 tables. Citation: "Richard Barnes, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu (2020). BusTr: Predicting Bus Travel Times from Real-Time Traffic. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. doi: 10.1145/3394486.3403376

Dynamic optimal travel strategies in intelligent stochastic transit networks

This paper addresses the search for a run-based dynamic optimal travel strategy, to be supplied through mobile devices (apps) to travelers on a stochastic multiservice transit network, which includes a system forecasting of bus travel times and bus arrival times at stops. The run-based optimal strategy is obtained as a heuristic solution to a Markovian decision problem. The hallmarks of this paper are the proposals to use only traveler state spaces and estimates of dispersion of forecast bus arrival times at stops in order to determine transition probabilities. The first part of the paper analyses some existing line-based and run-based optimal strategy search methods. In the second part, some aspects of dynamic transition probability computation in intelligent transit systems are presented, and a new method for dynamic run-based optimal strategy search is proposed and applied

Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques

Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operational and passenger behavior aspects. Therefore, it is highly expected that bus travel time prediction methods for different frequencies must have different outputs. Second, in the era of big data, applications of machine learning (ML) techniques in travel time prediction have significantly increased. However, there is no single ML model introduced in the literature that is the most accurate in predicting bus travel, especially with regard to bus service frequency. Consequently, the main objective of this study is to determine the most applicable route construction approach and most accurate tree-based ML technique for predicting bus travel time on high- and low-frequency bus routes. The following tree-based ML techniques were adopted in this study: chi-square automatic interaction detection (CHAID), random forest (RF), and gradient-boosted tree (GBT). According to the results, CHAID was selected as the most accurate model for predicting travel time on high-frequency routes, while GBT showed the best performance for low-frequency service. CHIAD analysis identified distance between stops and terminal departure behavior as the most significant factors of travel time on high-frequency routes. Moreover, we introduced the "key stop-based" route construction method for the first time, which is an accurate, reliable, and applicable method

A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions

Multi-headed self-attention mechanism-based Transformer model for predicting bus travel times across multiple bus routes using heterogeneous datasets

Bus transit is a crucial component of transportation networks, especially in urban areas. Bus agencies must enhance the quality of their real-time bus travel information service to serve their passengers better and attract more travelers. Various models have recently been developed for estimating bus travel times to increase the quality of real-time information service. However, most are concentrated on smaller road networks due to their generally subpar performance in densely populated urban regions on a vast network and failure to produce good results with long-range dependencies. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database and the vehicle probe data. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. This study developed a multi-headed self-attention mechanism-based Univariate Transformer neural network to predict the mean vehicle travel times for different hours of the day for multiple stations across multiple routes. In addition, we developed Multivariate GRU and LSTM neural network models for our research to compare the prediction accuracy and comprehend the robustness of the Transformer model. To validate the Transformer Model's performance more in comparison to the GRU and LSTM models, we employed the Historical Average Model and XGBoost model as benchmark models. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. Only the historical average bus travel time was used as the input parameter for the Transformer model. Other features, including spatial and temporal information, volatility measures (e.g., the standard deviation and variance of travel time), dwell time, expected travel time, jam factors, hours of a day, etc., were captured from our dataset. These parameters were employed to develop the Multivariate GRU and LSTM models. The model's performance was evaluated based on a performance metric called Mean Absolute Percentage Error (MAPE). The results showed that the Transformer model outperformed other models for one-hour ahead prediction having minimum and mean MAPE values of 4.32 percent and 8.29 percent, respectively. We also investigated that the Transformer model performed the best during different traffic conditions (e.g., peak and off-peak hours). Furthermore, we also displayed the model computation time for the prediction; XGBoost was found to be the quickest, with a prediction time of 6.28 seconds, while the Transformer model had a prediction time of 7.42 seconds. The study's findings demonstrate that the Transformer model showed its applicability for real-time travel time prediction and guaranteed the high quality of the predictions produced by the model in the context of a complicated extensive transportation network in high-density urban areas and capturing long-range dependencies.Includes bibliographical references

Hyperconnected parcel logistic hubs

Hyperconnected Parcel Logistic Hubs Shannon Buckley 144 Pages Advised By Dr. Benoit Montreuil This thesis focuses on the design of a new era of hub in the parcel logistics industry. Parcel logistics hubs (hubs) are the connective tissue in parcel logistics networks, sorting and transferring parcels from one vehicle to another as quickly as possible. However, as the demand for eCommerce surges, customer expectations for delivery speed rise and as the COVID pandemic creates disruptions throughout supply chains, new solutions are needed to ease the strain on the system. Current parcel logistics hubs are massive facilities filled to the brim with miles of fixed conveyor systems and expensive sorting machines. These hubs were built to handle large volumes of parcels at extremely fast speeds. However, with their fixed resources, they must have all operations finely scheduled and are not able to respond dynamically to disruptions such as an unexpected wave of parcels, resulting in backlogs and unhappy customers. In this thesis we help the transition away from this old, outdated hub design towards, dynamic, flexible hyperconnected parcel logistics hubs. In Chapter 2 we introduce a method for dynamically updating forecasts of the demand that hubs will face in the near future. We describe the new method and then compare it to existing methods with computational experiments. In Chapter 3 we present a pilot design for a new hyperconnected parcel logistics hub called the robotic logistics hub. We introduce designs for the layout as well as the operations and control of the hub, and finish with a comparative study of our hub’s performance against an existing hub from our industry partner. In Chapter 4 we present our novel simulation platform built to enable the analysis of new technology such as the robotic logistics hub. The simulator uses a hybrid discrete event / agent based modelling approach as well as a unique modular construction to allow for a highly flexible tool capable of providing deep insight into many facets of the proposed robotic logistics hub.Ph.D

Sequence modelling using deep learning approaches for spatiotemporal public transport data.

Encouraging the use of public transport is essential to combat congestion and pollution in an urban environment. To achieve this, the reliability of public transport arrival time prediction should be improved, as this is often requested by passengers. This will make the use of urban bus networks more convenient for passengers and, thus, will play a crucial role in shifting traffic to public transport. Ultimately, this will alleviate pollution and congestion and save a substantial amount of cost to society associated with the use of private cars. Here, the overarching objective was to investigate novel prediction methods and improve predictions for urban bus networks with a focus on short-horizon predictions. ETA predictions are unreliable due to the lack of good quality historical data, while ‘live’ positions in mobile apps suffer from delays in data transmission. The assessment of different of data quality regimes on the next-step prediction accuracy of Recurrent Neural Networks (RNN) showed that that without data cleaning, model predictions can give false confidence if mean errors are used, highlighting the importance of a holistic assessment of the results. It was demonstrated that noisy data is a problem and simple but effective approaches to address these issues are discussed. It became apparent that RNNs are exceptionally good at predicting stationary positions at either end of a journey. The maximum model improvement of the Sharpe ratio compared to noisy data was 4.71%. This provides insight into the value of addressing data quality issues in urban transport data to enable better predictions and improve the passenger experience. Furthermore, a comparison of different target representations was tested by encoding targets as unconstrained geographical coordinates, progress along a known trajectory, or ETA at the next two stops. The target representation was shown to affect the accuracy of the prediction by constraining the prediction space and reduced the prediction error from 244.8 to 142.3 m for the Long Short-Term Memory (LSTM) network. This error was further reduced if an ETA was predicted and if a distance is estimated from the ETA error resulted in a a reduction to 4.5 and 14.5 m for the next 2 stops on the route. Due to the observed lack of data quality, a method was to developed for synthesising data, using a reference curve approach derived from very limited real-world data without a reliable ground truth. This approach allows the controlled introduction of artefacts and noise to simulate their impact on prediction accuracy. To illustrate these impacts, a RNN next-step prediction was used to compare different scenarios in two different UK cities. Two model architectures were used as comparison: a Gated Unit and a LSTM model. Hybrid data was generated where real-world and synthetic data was mixed. When compared to the inference of a model trained purley on synthetic data, the error was reduced from 53.5 to 47.4 m for the LSTM and from 53.4 to 44.0 m for the GRU. The results show that realistic data synthesis is possible, allowing controlled testing of predictive algorithms. Urban traffic networks are interconnected systems that behave in complex ways to any disturbance. As urban buses operate in such networks and are influenced by traffic within this system, estimated arrival time (ETA) predictions can be challenging and are often inaccurate. To enable the use of network-wide data, a novel model architecture was developed. This attention-mechanism based predictor incorporated the states of other vehicles in the network by encoding their positions using gated recurrent units (GRU) of the individual bus line to encode their current state. By muting specific parts of the imputed information, their impact on prediction accuracy were estimated on a subset of the available data. The results showed that a network-based predictor outperforms models based on a single vehicle or all vehicles of a single line. However, a model limited to vehicles of the same line ahead of the target was the best performing model, suggesting that the incorporation of additional data can have a negative impact on the prediction accuracy if it does not add any useful information. This could be caused by poor data quality, but also by a lack of interaction between the included lines and the target line. The technical aspects of this architecture are challenging and resulted in a very inefficient training procedure. It can be expected that if a more efficient training regime is developed or the model is trained for a longer time, usable predictive accuracy can be achieved

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency