A New Big Data Approach to Understanding General Traffic Impacts on Bus Passenger Delays

[Abstract:] This paper presents a new method to quantify the potential user time savings if the urban bus is given preferential treatment, changing from mixed traffic to an exclusive bus lane, using a big data approach. The main advantage of the proposal is the use of the high amount of information that is automatically collected by sensors and management systems in many different situations with a high degree of spatial and temporal detail. These data allow ready adjustment of calculations to the specific reality measured in each case. In this way, we propose a novel methodology of general application to estimate the potential passenger savings instead of using simulation or analytical methods already present in the literature. For that purpose, in the first place, a travel time prediction model per vehicle trip has been developed. It has been calibrated and validated with a historical series of observations in real-world situations. This model is based on multiple linear regression. The estimated bus delay is obtained by comparing the estimated bus travel time with the bus travel time under free-flow conditions. Finally, estimated bus passenger time savings would be obtained if an exclusive bus lane had been implemented. An estimation of the passenger’s route in each vehicle trip is considered to avoid average value simplifications in this calculation. A case study is conducted in A Coruña, Spain, to prove the methodology's applicability. The results showed that 18.7% of the analyzed bus trips underwent a delay exceeding 3 min in a 2,448 m long corridor, and more than 33,000 h per year could have been saved with an exclusive bus lane. Understanding the impact of different factors on transit and the benefits of a priority bus system on passengers can help city councils and transit agencies to know which investments to prioritize given their limited budget.The authors would like to thank Compañía de Tranvías de La Coruña and Concello da Coruña for providing the data required to prepare this paper. This work was funded by grants RTI2018-097924-B-I00, PID2021-128255OB-I00 and PRE2019-089651, funded by MCIN/AEI/10.13039/501100011033 and by ERDF/EU and ESF/EU

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

Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.Comment: 34 pages, 12 figures, 5 table

Arterial traffic signal optimization: a person-based approach

This paper presents a traffic responsive signal control system that optimizes signal settings based on minimization of person delay on arterials. The system's underlying mixed integer linear program minimizes person delay by explicitly accounting for the passenger occupancy of autos and transit vehicles. This way it can provide signal priority to transit vehicles in an efficient way even when they travel in conflicting directions. Furthermore, it recognizes the importance of schedule adherence for reliable transit operations and accounts for it by assigning an additional weighting factor on transit delays. This introduces another criterion for resolving the issue of assigning priority to conflicting transit routes. At the same time, the system maintains auto vehicle progression by introducing the appropriate delays for when interruptions of platoons occur. In addition to the fact that it utilizes readily available technologies to obtain the input for the optimization, the system's feasibility in real-world settings is enhanced by its low computation time. The proposed signal control system was tested on a segment of San Pablo Avenue arterial located in Berkeley, California. The findings have shown the system's capability to outperform static optimal signal settings and have demonstrated its success in reducing person delay for bus and in some cases even auto users

Transit Demand Estimation And Crowding Prediction Based On Real-Time Transit Data

With an increasing number of intelligent analytic techniques and increasing networking capabilities, municipal transit authorities can leverage real-time data to estimate transit volume and predict crowding conditions. We introduce a proactive Transit Demand Estimation and Prediction System (TraDEPS) – an approach that has the potential to prevent crowding and improve transit service, by measuring the transit activity (the number of passengers on the individual modes of public transportation and the demand on a route), and estimating crowding levels at a given time. This system utilizes a combination of real-time data streams from multiple sources, a predictive model and data analytics for transit management. The problem of transit crowding is translated into transit activity prediction, as the latter is a straightforward indicator of the former. This thesis delivers the following contributions: (1) A crowding prediction model. (2) A system supporting the methodology. (3) A feature which displays different crowding level conditions of a route on a web map

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

Analyzing travel time reliability of a bus route in a limited data set scenario: A case study

In this information era commuters prefer to know a reliable travel time to plan ahead of their journey using both public and private modes. In this direction reliability analysis using the location data of the buses is conducted in two folds in the current work; (i) Reliability analysis of a public transit service at route level, and (ii) Travel time reliability analysis of a route utilizing the location data of the buses. The reliability parameters assessed for public transit service are headway, passenger waiting time, travel speed, and travel time as per the Service Level Benchmarks for Urban Transport by the National Urban Transport Policy, Government of India. And travel time reliability parameters such as Buffer Time Index, Travel Time Index, and Planning Time Index are assessed as per Federal Highway Administration, Department of Transportation, U S. The study is conducted in Tumakuru city, India for a significant bus route in a limited data sources scenario. The results suggest that (i) the Level of Service of the public transit service needs improvement. (ii)around 30% excess of average travel time is needed as buffer time. (iii) more than double the amount of free flow travel time must be planned during peak hours and in the worst case. In the future, the analysis conducted for the route can be extended for citywide performance analysis in both folds. Also, the same method can be applied to cities with similar demographics and traffic-related infrastructure.Comment: 10 pages, 7 figures, 6 table

Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces