Statistics for Spatial Data.

http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

Traffic Flow Prediction With Big Data: A Deep Learning Approach

This article shows how the evolution of multi-commodity traffic flows over complex networks can be predicted over time, based on a simple macroscopic computer representation of traffic flow that is consistent with the kinematic wave theory under all traffic conditions. The method does not use ad hoc procedures to treat special situations. After a brief review of the basic model for one link, the article describes how three-legged junctions can be modeled. It then introduces a numerical procedure for networks, assuming that a time-varying origin-destination (O-D) table is given and that the proportion of turns at every junction is known. These assumptions are reasonable for numerical analysis of disaster evacuation plans. The results are then extended to the case where, instead of the turning proportions, the best routes to each destination from every junction are known at all times. For technical reasons explained in the text, the procedure is more complicated in this case, requiring more computer memory and more time for execution. The effort is estimated to be about an order of magnitude greater than for the static traffic assignment problem on a network of the same size. The procedure is ideally suited for parallel computing. It is hoped that the results in the article will lead to more realistic models of freeway flow, disaster evacuations and dynamic traffic assignment for the evening commute.

The cell transmission model, part ii: network traffic

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Data-Driven Intelligent Transportation Systems: A Survey

Bus headway in a rural area usually is much larger than that in an urban area. Providing real-time bus arrival information could make the public transit system more user-friendly and thus enhance its competitiveness among various transportation modes. As part of an operational test for rural traveler information systems currently ongoing in Blacksburg, Virginia, an experimental study has been conducted on forecasting the arrival time of the next bus with automatic vehicle location techniques. The process of developing arrival time estimation algorithms is discussed, including route representation, global positioning system (GPS) data screening for identifying data quality and delay patterns, algorithm formulation, and development of measures of performance. Whereas GPS-based bus location data are adopted in all four algorithms presented, the extent to which other information is used in these algorithms varies. In addition to bus location data, information relevant to the performance of an algorithm includes scheduled arrival time, delay correlation, and waiting time at time-check stops. The performance of an algorithm using different levels of information is compared against three criteria: overall precision, robustness, and stability. Results show that at the site where the study is being conducted, the dwell time at time-check stops is most relevant to the performance of an algorithm.

/pdf/experimental-study-of-real-time-bus-arrival-time-prediction-47deuv6bkx.pdf

Experimental Study of Real-Time Bus Arrival Time Prediction with GPS Data:

An enhanced 0-1 mixed-integer linear programming formulation based on the cell-transmission model is proposed for the traffic signal optimization problem. This formulation has several features that are currently unavailable in other existing models developed with a similar approach, including the components for handling the number of stops, fixed or dynamic cycle length and splits, and lost time. The problem of unintended vehicle holding, which is common in analytical models, is explicitly treated. The formulation can be utilized in developing strategies for adaptive traffic-control systems. It can also be used as a benchmark for examining the convergence behavior of heuristic algorithms based on the genetic algorithm, fuzzy logic, neural networks, or other approaches that are commonly used in this field. The discussion of extending the proposed model to capture traffic signal preemption in the presence of emergency vehicles is given. In terms of computational efficiency, the proposed formulation has the least number of binary integers as compared with other existing formulations that were developed with the same approach.

An enhanced 0-1 mixed-integer LP formulation for traffic signal control

This paper describes a freeway incident detection scheme that does not rely on complicated theories. The procedure compares the occupancy information recorded by two neighboring loop detectors to determine whether an incident has occurred in the intervening segment, using a recipe that is directly related to an intrinsic property of delay-inducing incidents. The method, which can also signal the termination of a detected incident, assumes that any significant bottlenecks are located outside the segment in question: i.e. that the segment is “homogeneous”. Independent of complicated theories, the proposed method can be applied with little calibration to any site despite the presence of detector errors and statistical fluctuations. It is also possible to use site-independent (default) parameters, although one should then expect a somewhat inferior performance. Tests with five sets of 30 s data from three different sites in the United States, Canada, and the Netherlands, using the recommended default parameters, were encouraging. The resulting graphical displays included in this paper clearly illustrate the scheme's effectiveness in distinguishing non-recurrent from recurrent congestion.

A simple detection scheme for delay-inducing freeway incidents

Many existing algorithms for bus arrival time prediction assume that buses travel at free-flow speed in the absence of congestion. As a result, delay incurred at one stop would propagate to downstream stops at the same magnitude. In reality, skilled bus operators often constantly adjust their speeds to keep their bus on schedule. This paper formulates a Markov chain model for bus arrival time prediction that explicitly captures the behavior of bus operators in actively pursuing schedule recovery. The model exhibits some desirable properties in capturing the schedule recovery process. It guarantees provision of the schedule information if the probability of recovering from the current schedule deviation is sufficiently high. The proposed model can be embedded into a transit arrival time estimation model for transit information systems that use both real-time and schedule information. It also has the potential to be used as a decision support tool to determine when dynamic or static information should be used.

Wei-Hua Lin

Papers

Data-Driven Intelligent Transportation Systems: A Survey

Experimental Study of Real-Time Bus Arrival Time Prediction with GPS Data:

An enhanced 0-1 mixed-integer LP formulation for traffic signal control

A simple detection scheme for delay-inducing freeway incidents

Modeling schedule recovery processes in transit operations for bus arrival time prediction