A Continuous Dynamic Traffic Assignment Model From Plate Scanning Technique

[EN] This paper presents a methodology for the dynamic estimation of traffic flows on all links of a network from observable field data assuming the first-in-first-out (FIFO) hypothesis. The traffic flow intensities recorded at the exit of the scanned links are propagated to obtain the flow waves on unscanned links. For that, the model calculates the flow-cost functions through information registered with the plate scanning technique. The model also responds to the concern about the parameter quality of flow-cost functions to replicate the real traffic flow behaviour. It includes a new algorithm for the adjustment of the parameter values to link characteristics when its quality is questionable. For that, it is necessary the a priori study of the location of the scanning devices to identify all path flows and to measure travel times in all links. A synthetic network is used to illustrate the proposed method and to prove its usefulness and feasibilityRivas, A.; Gallego, I.; Sánchez-Cambronero, S.; Ruiz-Ripoll, L.; Barba, R. (2016). A Continuous Dynamic Traffic Assignment Model From Plate Scanning Technique. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1132-1141. https://doi.org/10.4995/CIT2016.2015.4215OCS1132114

[[alternative]]A Study on the Estimation of Travel Time and Installation Guideline for Vehicle Detector on Singalized Arterials

計畫編號：NSC94-2211-E032-015研究期間：200508~200607研究經費：404,000[[abstract]]為了獲取有效的即時路況資訊，如何挑選合適的偵測器佈設位置使得演算法能得到 有效的資料來源，進而計算出最準確的旅行時間，是目前重要的研究課題之一。本研究 旨在藉由車流模擬實驗與解析性模式等方式，找出適合國內車流特性的偵測器佈設策 略，同時驗證不同的路段旅行時間演算法或推估模式在台灣地區的適用程度，希望能找 出適合本土特性的路段旅行時間推估模式及偵測器佈設策略，以期能有效掌握即時、方 便的交通資訊，使交通管理單位能以有效率、低成本的方式研訂相關交通偵測設施佈設 策略與交通管理措施，進一步提供用路人可靠且便捷的交通資訊服務。[[sponsorship]]行政院國家科學委員

A methodology (CUPRITE) for urban network travel time estimation by integrating multisource data

Travel time is an important network performance measure and it quantifies congestion in a manner easily understood by all transport users. In urban networks, travel time estimation is challenging due to number of reasons such as, fluctuations in traffic flow due to traffic signals, significant flow to/from mid-link sinks/sources, etc. In this research a methodology, named CUmulative plots and PRobe Integration for Travel timE estimation (CUPRITE), has been developed, tested and validated for average travel time estimation on signalized urban network. It provides exit movement specific link travel time and can be applied for route travel time estimation. The basis of CUPRITE lies in the classical analytical procedure of utilizing cumulative plots at upstream and downstream locations for estimating travel time between the two locations. The classical procedure is vulnerable to detector counting error and non conservation of flow between the two locations that induces relative deviation amongst the cumulative plots (RD). The originality of CUPRITE resides in integration of multi-source data: detector data and signal timings from different locations on the network, and probe vehicle data. First, cumulative plots are accurately estimated by integrating detector and signal timings. Thereafter, cumulative plots are integrated with probe vehicle data and RD issue is addressed. CUPRITE is tested rigorously using traffic simulation for different scenarios with different possible combinations of sink, source and detector error. The performance of the proposed methodology has been found insensitive to percentage of sink or source or detector error. For a link between two consecutive signalized intersections and during undersaturated traffic condition, the concept of virtual probe is introduced and travel time can be accurately estimated without any real probe. For oversaturated traffic condition, CUPRITE requires only few probes per estimation interval for accurate travel time estimation. CUPRITE is also validated with real data collected from number plate survey at Lucerne, Switzerland. Two tailed t-test (at 0.05 level of significance) results confirm that travel time estimates from CUPRITE are statistically equivalent to real estimates from number plate survey. The testing and validation of CUPRITE have demonstrated that it can be applied for accurate and reliable travel time estimation. The current market penetration of probe vehicle is quite low. In urban networks, availability of a large number of probes per estimation interval is rare. With limited number of probe vehicles in urban networks, CUPRITE can significantly enhance the accuracy of travel time estimation

Link Travel Time Estimation Based on Network Entry/Exit Time Stamps of Trips

This dissertation studies the travel time estimation at roadway link level using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation-Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times

대용량 자료를 이용한 네트워크 기반 도시간 경로통행시간 예측

학위논문 (박사)-- 서울대학교 환경대학원 : 환경계획학과, 2015. 2. 이영인.오늘날의 도시교통정보를 한 마디로 요약하자면 대용량 자료일 것이다. 첨단기술을 기반으로 하는 스마트 폰과 21세기 지능형교통체계(Intelligent Transport System, 이하 ITS)의 정보수집단을 통해 수집되는 방대한 양의 다양한 교통정보는 대용량 자료의 많은 부분을 점유하고 있으며, ITS분야의 자료환경을 소량의 실시간 자료에서 방대한 이력자료를 포함하는 대용량 자료로 변화시켰다. 이러한 자료환경의 변화에 따라 최근 ITS분야에서는 대용량 자료를 수집․관리․분석하기 위하여 첨단자료관리시스템(Advanced Data Management System, 이하 ADMS)이 도입되고 있다. ITS의 사회적 편익은 장래 교통상태의 예측을 통한 사전 교통류 관리 및 동적 교통정보제공으로 극대화된다. 따라서 교통상태의 예측은 ITS의 주요 요소 중 하나이며, ITS에서 교통상태 예측시스템은 첨단교통관리시스템과 첨단교통정보시스템의 핵심 하위시스템 중 하나이다. 이와 같이 교통상태 예측기술은 교통상태 예측시스템의 성능과 더불어 ITS의 편익 증대에 있어 밀접한 관련이 있다. 따라서 20세기 말에 ITS가 도입된 이후로 ITS 분야의 예측기술은 다양한 예측모형의 개발을 통해 지속적으로 발전하고 있으며, 하나의 학문분야로 자리잡았다. 기존의 고도화된 예측모형은 예측 정확도 향상이라는 목표를 달성하였으나, 다음의 한계를 가지고 있다. 첫째, 기존모형은 ITS 예측분야의 고질적 문제인 장래 상태의 불확실성을 극복하지 못 하였기 때문에 단기예측의 수준에서 벗어나지 못 하고 있다. 둘째, 많은 경우에 있어 실시간 자료를 이용하도록 설계되었기 때문에 ADMS와 같은 자료관리시스템에 탑재되어 실시간 자료와 대용량 이력자료를 이용하여 교통상태의 예측에 적용하기 어려운 구조적 문제가 있다. 마지막으로 고도화된 모형들은 ITS 시스템에 탑재․운영시 새로운 문제를 발생시키고 있다. 고도화된 모형은 모형의 구조변경, 입․출력 자료의 변경, 파라미터 값의 재정산 등에 교통류의 행태와 예측 모델링에 대한 깊은 이해를 필요로 하기 때문에 예측 모델링 경험이 부족한 현장의 운영요원에게 새로운 장애가 되고 있다. 본 연구에서는 ITS 예측분야의 지속적 도전 과제인 장래 상태의 불확실성을 극복하고 예측영역의 확장을 위하여 실시간 및 대용량 이력 교통자료를 이용한 교통정보 예측기(Forecaster)인 KJC 예측기를 개발하였으며, 다음의 목표를 달성하도록 설계되었다. 첫째, 장래 상태의 불확실성을 극복하기 위한 방안이 고려되었다. 불확실성을 감소시키기 위하여 입력자료의 공간적 영역을 기존의 지점 또는 구간에서 도로망으로 확장하고, 도로망의 소통상태를 이용하여 장래 상태의 불확실성을 감소시켰다. 둘째, KJC 예측기는 ADMS와 같은 자료관리시스템에 탑재되어 도로망 소통상태를 예측하고, 예측된 도로망 소통상태를 이용하여 단․중․장거리 경로통행시간을 예측하도록 개발되었다. 따라서 보다 적극적이고 전술적인 첨단교통관리와 동적 중․장거리 통행시간 정보제공에 활용할 수 있도록 하였다. 이상의 목적으로 개발된 KJC 예측기는 이력자료에 내재된 장래 교통상태 정보를 탐색 및 구축하기 위한 지식탐색 모듈, 군집화 모형을 이용한 의사결정 그룹화 모듈, 그리고 사례기반 추론을 기반으로하는 예측 의사결정 모듈로 구성된다. 3개 모듈은 입․출력 자료구조의 용이한 변경, 결측자료의 자동처리, 파라미터 값의 자동정산, 연산수행속도 등을 고려하여 개발되었다. 본 연구에서 개발된 KJC 예측기의 성능은 대용량 자료환경에서 평가되었다. 경부고속도로 서울-대전 구간을 대상으로 8개월간 약 4억건의 통행사슬 자료를 이용하여 도로구간 소통상태 및 경로통행시간 이력자료를 구축하였으며, 구축된 자료는 총 18,768,960건이다. 다각적인 종합평가 결과, 개발된 교통정보 예측기는 매우 빠른 연산수행속도를 보이면서 장래 6시간까지 도로망의 소통상태를 합리적으로 예측하였다. 그리고 개발된 예측기로 추정된 도시간 경로통행시간의 정확도는 모든 단․중․장거리 통행시간 시나리오에서 기존의 경로통행시간 예측기법들에 비하여 매우 우수하게 나타났다.Abstract Network-based Intercity Path Travel Time Forecasting Using Large-scale Data Chang, Hyun-Ho Department of Environmental Planning The Graduate School of Environmental Studies Seoul National University Todays urban and transportation information can be summarized simply with the words big data. Unimaginably tremendous quantities of information collected by smart-phones and information devices of 21C intelligent transportation systems (ITS) based on edge technology accounts for much of this big data, and changes in the data environment of ITS from small real-time data to big data mean that it now includes vast quantities of historical data as well. With these changes in data environments, advanced data management systems (ADMS) have recently been introduced to process, store, and analyze big data in the field of ITS. The forecasting of traffic conditions along road networks is one of the essential factors with regard to ITS, as the social benefits of ITS can be maximized by proactive traffic management and the provision of dynamic traffic information based on the forecasting of traffic conditions. The traffic state forecasting system is one of the kernel sub-systems of an advanced traffic management system and the advanced traffic information system in ITS. Hence, forecasting technology to generate future states is closely related to increments in ITS benefits and to the performance of the traffic state forecasting system. Various forecasting models, from simple and conventional to refined and sophisticated, have therefore been proposed since ITSs were widely introduced at the end of the twentieth century. Although existing advanced models have essentially achieved the common goal of ITS forecasting with improvements in forecasting accuracy, they have several chronic or emerging problems to be solved. First, the temporal prediction horizon of the models in most cases still operate on the short termthey cannot from the perspective of long-term forecasting overcome uncertainties in future states, and this remains an unsolved problem in the ITS forecasting area. Second, their structures are not suitable when they are coupled with a data management system such as ADMS and then used to estimate future states using both real-time and historical data, as they are in many cases designed to utilize only real-time data. Lastly, many sophisticated models becoming associated with obstacles which require field staff to manage. These models inevitably require the field staff to possess a deep understanding of the behaviors of traffic flows and forecasting model and then to manipulate the operational factors of these entities, such as structural changes of algorithms, in-and-out alterations of data, recalibrations of parameter values and other such actions. In this thesis, a traffic information forecaster termed the KJC forecaster is developed based on a combination of k-nearest neighbor nonparametric regression and j-clustering using both real-time and historical data. First, a conquest solution to address the uncertainties of future states is proposed. In order to reduce the uncertainties of future states, the spatial concept of a forecasting model is expanded from an isolated location or link to a road network, after which traffic states, such as link travel speeds and link probe volumes, of the road network are utilized as the inputs to the forecaster. Second, KJC forecaster is designed to be used in conjunction with data management systems such as ADMS and to estimate the future traffic conditions of road networks. This can in turn be used to generate short-, middle-, and long-distance path travel times. The forecaster, therefore, is at the very least suitable for more proactive and tactical advanced traffic management and especially for dynamic intercity path travel times. The KJC forecaster consists of three modules: a knowledge discovery module to search for and compile the information on future traffic states included in the historical data, a clustering module to determine decision-making groups, and a forecasting decision-making module which is based on case-based reasoning. The three modules were developed while considering operational requirements such as multivariate in-and-out data, easy alterations of inputs and outputs, the automatic processing of missing data, the automatic calibration of parameter values, as well as high-speed computing that is actually faster than real-time. The performance of the traffic information forecaster was tested under the circumstances of large-scale data and the test bed was Seoul-Daejeon road section, 142 km, of Gyeongbu motorway. The historical database used, with a data size of 18,768,960 items, was composed of link-based traffic flow information and path travel times which were compiled using nearly four hundred million instances of trip chain data for eight months. This data was collected by means of dedicated short range communications technology. The results show that KJC forecaster estimates accurate traffic conditions, at least from a forecasting perspective, of road networks up to six hours in the future, with high-speed computations. In addition, the forecaster is clearly superior to two compared path travel time methods, instantaneous and experience-based, in terms of prediction accuracy of the path travel time. Therefore, it is clear that KJC forecaster as proposed in this thesis is a promising multivariate long-term traffic flow forecasting approach which is feasible for use with large-scale data. keywords : Advanced Data Management System, Large-scale Data, k-Nearest Neighbor Nonparametric Regression, j-Clustering, Long-term forecasting, Traffic Condition of Road Network, Intercity Path Travel Time Student Number : 2008-30675Docto

Performing Short-Term Travel Time Prediction on Arterials

As urban centers become larger and more densely developed, their roadway networks tend to experience more severe congestion for longer periods of the day and increasingly unreliable travel times. Proactive traffic management (PTM) strategies such as proactive traffic signal control systems and advanced traveler information systems provide the potential to cost effectively improve road network operations. However, these proactive management strategies require an ability to accurately predict near-future traffic conditions. Traffic conditions can be described using a variety of measures of performance and travel time is one of the most valued by both travelers and transportation system managers. Consequently, there exists a large body of literature dedicated to methods for performing travel time prediction. The majority of the existing body of research on travel time prediction has focused on freeway travel time prediction using fixed point sensor data. Predicting travel times on signalized arterials is more challenging than on freeways mainly as a result of the higher variation of travel times in these environments. For both freeways and arterial environments, making predictions in real-time is more challenging than performing off-line predictions, mainly because of data availability issues that arise for real-time applications. Recently, Bluetooth detectors have been utilized for collecting both spatial (i.e. travel time) and fixed point (e.g. number of detections) data. Bluetooth detectors have surpassed most of the conventional travel time measuring techniques in three main capacities: (i) direct measurement of travel time, (ii) continuous collection of travel times provides large samples, and (iii) anonymous detection. Beside these advantages, there are also caveats when using these detectors: (i) the Bluetooth obtained data include different sources of outliers and measurement errors that should be filtered out before the data are used in any travel time analysis and (ii) there is an inherent time lag in acquiring Bluetooth travel times (due to the matching of the detections at the upstream and downstream sensors) that should be carefully handled in real-time applications. In this thesis, (1) the magnitude of Bluetooth travel time measurement error has been examined through a simulation framework; (2) a real-time proactive outlier detection algorithm, which is suitable for filtering out data anomalies in Bluetooth obtained travel times, has been proposed; (3) the performance of the existing real-time outlier detection algorithms has been evaluated using both field data and simulation data; and (4) two different data-driven methodologies, that are appropriate for real-time applications, have been developed to predict near future travel times on arterials using data obtained from Bluetooth detectors. The results of this research demonstrate that (1) although the mean Bluetooth travel time measurement error is sufficiently close to zero across all the examined traffic conditions, for some situations the 95% confidence interval of the mentioned error approaches 35% of the true mean travel time; (2) the proposed proactive filtering algorithm appropriately detects the Bluetooth travel time outliers in real time and outperforms the existing data-driven filtering techniques; (3) the performance of different outlier detection algorithms can be objectively quantified under different conditions using the developed simulation framework; (4) the proposed prediction approaches significantly improved the accuracy of travel time predictions for 5-minutre prediction horizon. The daily mean absolute relative errors are improved by 18% to 24% for the proposed k-NN model and 8% to 14% for the proposed Markov model; (5) prevailing arterial traffic state and its transition through the course of the day can be adequately modeled using data obtained from Bluetooth technology