Deep Learning and Bayesian Calibration Approach to Hourly Passenger Occupancy Prediction in Beijing Metro: A Study Exploiting Cellular Data and Metro Conditions

In In burgeoning urban landscapes, the proliferation of the populace necessitates swift and accurate urban transit solutions to cater to the citizens' commuting requirements. A pivotal aspect of fostering optimized traffic management and ensuring resilient responses to unanticipated passenger surges is precisely forecasting hourly occupancy levels within urban subway systems. This study embarks on delineating a two-tiered model designed to address this imperative adeptly: 1. Preliminary Phase - Employing a Feed Forward Neural Network (FFNN): In the initial phase, a Feed Forward Neural Network (FFNN) is employed to gauge the occupancy levels across various subway stations. The FFNN, a class of artificial neural networks, is well-suited for this task because it can learn from the data and make predictions or decisions without being explicitly programmed to perform the task. Through a series of interconnected nodes, known as neurons, arranged in layers, the FFNN processes the input data, adjusts its weights based on the error of its predictions, and optimizes the network for accurate forecasting. For the random process of occupation levels in time and space, this phase encapsulates the so-called process filtration, wherein the underlying patterns and dynamics of subway occupancy are captured and represented in a structured format, ready for subsequent analysis. The estimates garnered from this phase are pivotal and form the foundation for the subsequent modelling stage. 2. Subsequent Phase - Implementing a Bayesian Proportional-Odds Model with Hourly Random Effects: With the estimates from the FFNN at disposal, the study transitions to the subsequent phase wherein a Bayesian Proportional-Odds Model is utilized. This model is particularly adept for scenarios where the response variable is ordinal, as in the case of occupancy levels (Low, Medium, High). The Bayesian framework, underpinned by the principles of probability, facilitates the incorporation of prior probabilities on model parameters and updates this knowledge with observed data to make informed predictions. The unique feature of this model is the incorporation of a random effect for hours, which acknowledges the inherent variability across different hours of the day. This is paramount in urban transit systems where passenger influx varies significantly with the hour. The synergy of these two models facilitates calibrated estimations of occupancy levels, both conditionally (relative to the sample) and unconditionally (on a detached test set). This dual-phase methodology furnishes analysts with a robust and reliable insight into the quality of predictions propounded by this model. This, in turn, avails a data-driven foundation for making informed decisions in real-time traffic management, emergency response planning, and overall operational optimization of urban subway systems. The model expounded in this study is presently under scrutiny for potential deployment by the Beijing Metro Group Ltd. This initiative reflects a practical stride towards embracing sophisticated analytical models to ameliorate urban transit management, thereby contributing to the broader objective of fostering sustainable and efficient urban living environments amidst the surging urban populace

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

Metro systems : Construction, operation and impacts

Peer reviewedPublisher PD

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

Modeling office firm dynamics in an agent-based micro simulation framework : methods and empirical analysis

Office firms represent a large share of economic activities, especially in the sector of professional services. In general, firms will follow an evolutionary cycle comprising the dynamics of starting-up, finding a location to establish their business, growing or declining, relocating and going out of business. The underlying approach taken in this research project relies on the idea that the evolution of office firms is strongly influenced by the urban environment. Traditionally, the specific relationship between transportation and land use has been examined in the framework of aggregate integrated land use-transportation (LUTI) models. However, the field is moving toward a more disaggregate approach, based on concepts of micro simulation and agent-based models. These are built on behaviorally richer concepts for examining firm dynamics, such as firm demography. The aim of this research project is to contribute to this emerging field by developing an agent-based modeling approach to simulate the evolution of office firms in time and space. To this end, a set of statistical/econometric models is used to investigate the relationships between specific firm demographic processes and the urban environment. The research project contributes to the existing literature by focusing on office firm demography and related land use and transportation influences, exploring alternative approaches to model office firm dynamics empirically, and using very detailed nationwide data from The Netherlands

A new spatial model for predicting multivariate counts : anticipating pedestrian crashes across neighborhoods and firm births across counties

textTransportation research regularly relies on data exhibiting both space and time dimensions. Thanks to the rise of smartphones, Bluetooth, and other devices, geo-referenced data collection enables application of more behaviorally realistic -- but complex -- models that account for spatial autocorrelation, temporal correlation, and possible time-space interactions (e.g., time-lagged effects from a neighboring unit's response). One promising area is crash count prediction, where crash frequencies (and severities) at zones, intersections, and along roadways will generally exhibit some spatial relationships, due to missing variables, causal mechanisms, and other ties. This dissertation work proposes and estimates a spatial multivariate count model and provides two case studies to implement such model. One case study is in the context of pedestrian-vehicle crash counts across zones in Austin, Texas, while accounting for network features (e.g., lane-miles and intersection density), land use factors (such as land use entropy and residential accessibility to commercial activities), population and job densities, and school access. The other case study pertains to new firm births by industries across U.S. counties while controlling for population density, agglomeration economies (e.g., percentage of firms with more than 100 people), wealth, and median age. The new model specification captures region-wide heterogeneity (thanks to extra variation introduced by the lognormal component in the mean crash-rate specification), correlations across two (or more) count types (in the same zone), and spatial autocorrelation among unobserved components. This new approach and associated application allow analysts to distinguish covariates' effects on multivariate crash and other counts from spatial spillover effects and cross-response correlations. This work adds to the literature by providing guidance on what types of specifications best reflect spatial count data while facilitating estimation (using large data sets) and illuminating the level and nature of spatial autocorrelation, multivariate correlation, and region-wide (latent) heterogeneity that exists in crash data after controlling for a host of observable factors.Civil, Architectural, and Environmental Engineerin

Discovery of Non-Persistent Motif Mixtures using MRST (Multivariate Rhythm Sequence Technique)

In this paper we present a prototype to discover the unsupervised repeating temporary perception in a time series. The purpose of this work is to control the case of random variable and to find out the measurements caused by the phenomena of simultaneous synchronization. The proposed model has used the non-parametric Bayesian technique to trace the motifs and their occurrences in the data documents. We introduce the Multivariate Rhythm Sequence Technique (MRST) method to find the rebound and repeated motifs and their instance in every document automatically and simultaneously. This model is used in wide range of applications and concentrates on datasets from different modalities.The video footages from non-dynamic cameras and data location bounded to the motif-mining server. The high semantic internal representation of the method gives advantage in operation such as event counting or analyse the sc8BA5;. We used the sample images and videos from New York City traffic data for experiments with and the results shows better performance than the existing motif mixtures analysis in the time series

Data Fusion for MaaS: Opportunities and Challenges

© 2018 IEEE. Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202