Development and application of dynamic models for predicting transit arrival times

Stochastic variations in traffic conditions and ridership often have a negative impact in transit operations resulting in the deterioration of schedule/headway adherence and lengthening of passenger wait times. Providing accurate information on transit vehicle arrival times is critical to reduce the negative impacts on transit users. In this study, models for dynamically predicting transit arrival times in urban settings are developed, including a basic model, a Kalman filtering model, link-based and stop-based artificial neural networks (ANNs) and Neural/Dynamic (ND) models. The reliability of these models is assessed by enhancing the microscopic simulation program CORSIM which can calculate bus dwell and passenger wait times based on time-dependent passenger demands and vehicle inter-departure times (headways) at stops. The proposed prediction models are integrated with the enhanced CORSIM individually to predict bus arrival times while simulating the operations of a bus transit route in New Jersey. The reliability analysis of prediction results demonstrates that ANNs are superior to the basic and Kalman filtering models. The stop-based ANN generally predicts more accurately than the link-based ANN. By integrating an ANN (either link-based or stop-based) with the Kalman filtering algorithm, two ND models (NDL and NDS) are developed to decrease prediction error. The results show that the performance of the ND models is fairly close. The NDS model performs better than the NDL model when stop-spacing is relatively long and the number of intersections between a pair of stops is relatively large. In the study, an application of the proposed prediction models to a real-time headway control model is also explored and experimented through simulating a high frequency light rail transit route. The results show that with the accurate prediction of vehicle arrival information from the proposed models, the regularity of headways between any pair of consecutive operating vehicles is improved, while the average passenger wait times at stops are reduced significantly

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

Transit Preferential Treatments at Signalized Intersections: Person-based Evaluation and Real-Time Signal Control

Efficient public transportation has the potential to relieve traffic congestion and improve overall transportation system performance. In order to improve transit services, Transit Preferential Treatments (TPT) are often deployed to give transit vehicles priority over other vehicles at an intersection or along a corridor. Examples of such treatments are exclusive bus lanes, queue jumper lanes, and signal priority strategies. The objective of this study is threefold: 1) perform a person-based evaluation of alternative TPTs when considered individually and in combination, 2) develop a bus travel time prediction model along a signalized arterial, and 3) develop a real-time signal control system, which minimizes total person delay at an isolated intersection accounting for stochasticity in transit vehicle arrivals. This study first develops analytical models to estimate person delay and person discharge flow when various spatial and time TPTs are present at signalized intersections with and without near-side bus stops. This part of the research has contributed to the modeling of traffic along signalized arterials by improving the previous models to evaluate various TPT strategies with and without nearside bus stops. Next, a robust method to predict bus travel time along a signalized arterial is developed. This part of the research contributes to the bus travel time prediction models by estimating the status of traffic signals using automated vehicle location (AVL) data. The model decomposes bus travel time along signalized arterials and infers trajectories of the transit vehicles. Finally, the real-time signal control system is developed to provide priority to transit vehicles by assigning weights to transit vehicle delays based on their passenger occupancies as part of the optimization objective function. The system optimizes the movements by minimizing total person delay at the intersection. The system estimates bus arrival time at the intersection stopline and uses the developed analyitical models in the first part of the research to evaluate the person delay measure. This part of the research contributes to the real-time signal control systems by providing a priority window to account for the stochasticity in bus arrival times

A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem is notably exacerbated when the bus system operates along a signalized corridor with unpredictable travel demand. To mitigate this challenge, we introduce a multi-strategy fusion approach for the longitudinal control of connected and automated buses. The approach is driven by a physics-informed deep reinforcement learning (DRL) algorithm and takes into account a variety of traffic conditions along urban signalized corridors. Taking advantage of connected and autonomous vehicle (CAV) technology, the proposed approach can leverage real-time information regarding bus operating conditions and road traffic environment. By integrating the aforementioned information into the DRL-based bus control framework, our designed physics-informed DRL state fusion approach and reward function efficiently embed prior physics and leverage the merits of equilibrium and consensus concepts from control theory. This integration enables the framework to learn and adapt multiple control strategies to effectively manage complex traffic conditions and fluctuating passenger demands. Three control variables, i.e., dwell time at stops, speed between stations, and signal priority, are formulated to minimize travel duration and ensure bus stability with the aim of avoiding bus bunching. We present simulation results to validate the effectiveness of the proposed approach, underlining its superior performance when subjected to sensitivity analysis, specifically considering factors such as traffic volume, desired speed, and traffic signal conditions

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC

A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

Effective bus travel time prediction is essential in transit operation system. An improved support vector machine (SVM) is applied in this paper to predict bus travel time and then the efficiency of the improved SVM is checked. The improved SVM is the combination of traditional SVM, Grubbs’ test method and an adaptive algorithm for bus travel-time prediction. Since error data exists in the collected data, Grubbs’ test method is used for removing outliers from input data before applying the traditional SVM model. Besides, to decrease the influence of the historical data in different stages on the forecast result of the traditional SVM, an adaptive algorithm is adopted to dynamically decrease the forecast error. Finally, the proposed approach is tested with the data of No. 232 bus route in Shenyang. The results show that the improved SVM has good prediction accuracy and practicality

Development and Evaluation of Bus Operation Control System Based on Cooperative Speed Guidance

Buses often have strong bunching or large interval tendency when traveling further along the route. To restrain this further deterioration of operation service, this paper developed a bus operation control system to dynamically adjust bus speed, bus dwell time, and traffic signal timings along the running path. In addition, a simulation platform was developed to evaluate the proposed control system with the actual data collected from bus route number 210 in Shanghai. The simulation results show that the proposed control system can mitigate the amplification trend of the headway deviation along the route to produce headways within a given tolerance

Link Travel Time Prediction Based on O-D Matrix and Neural Networks

Ühistranspordi kasutajad on tihtipeale huvitatud täpsest reisiajast seetõttu, et tõhusalt aega planeerida. Kuid ebaregulaarsete reisiaegade tõttu on seda üsna keeruline teha. Reisiaegade muutused võivad olla põhjustatud näiteks ilmastikuoludest, liiklusõnnetustest ja liiklusnõudlusest. Intelligentse transpordisüsteemi kaasamisega ühistranspordi süsteemi muutus hõlpsamaks bussireisi andmete kogumine, sealhulgas ka reisiaegade kogumine. Kogutud andmeid on võimalik kasutada tulevaste reiside prognoosimiseks, rakendades erinevaid teaduslikke meetodeid, näiteks Kalmani filtrit, masinõpet ja tehisnärvivõrke. Antud lõputöö eesmärgiks on luua tehisnärvivõrgu mudel, mis ennustab tiheda liiklusega teekonna reisiaega. Selleks kasutatakse algpunkt-sihtpunkt maatriksit, mis on koostatud sama teekonna kohta kogutud GPS informatsioonist. Ennustustäpsuse arvutamiseks kasutati antud lõputöös ruutkeskmist viga (RMSE). Tulemuste analüüs näitas, et antud mudel on piisav tegemaks tulevaste reisiaegade ennustusi.In public transportation system, commuters are often interested in getting accurate travel time information regarding trips in the future in order to plan their future schedules effectively. However, this information is often difficult to predict due to the irregularities in travel time which are caused by factors like future weather conditions, road accidents and fluctuations in traffic demand. With the introduction of Intelligent Transportation System into public transport system, it has been easy to collect data regarding bus trips such as travel times data. The data collected can be used to make predictions regarding trips in the future by applying scientific methods like Kalman filter, machine learning, and deep learning neural network. The goal of this thesis is to develop a neural network model for predicting travel time information of a busy route using Origin-Destination matrix derived from a historical GPS dataset of the same route. The prediction accuracy of the NN model developed in this thesis was measured using Root Mean Square Error (RMSE). Analysis of the result showed that the model is sufficient for making predictions of travel time for trips in the future

Simulation-based evaluation of advanced public transportation information systems (APTIS)

Despite the great success and the broad diffusion of Advanced Public Transportation Information System (APTIS), there is a lack of studies, in the literature, investigating “systematically” how and to what extend such systems can affect network performances and travelers’ path choices. In this paper, with respect to the realistic case study of the city of Naples (South-Italy), we investigate the impacts of information offer in a Public Transportation (PT) network under different network condition, i.e. irregular vs. regular services, congested vs. un-congested lines. The focus is on APTIS deploying shared en-route descriptive information. The presented results are based on the simulation of the three main components of the PT system, namely the network performances, the information provider (i.e. the Operation Control Center) and the travelers. The simulation of these components and their interaction is achieved using different modeling approach: the schedule-based approach for the network representation and the traffic assignment, a statistical model based on the Kalman filter for the prediction of the link travel times within the simulation period, and behavioral discrete choice models, following the Random Utility Theory, for simulating travelers’ behavior