Highway Capacity Manual改訂の動向--テイラ-教授の講演より

https://www.ce.utexas.edu/prof/bhat/ABSTRACTS/AMAR_review_article.pdf

Analytic methods in accident research: Methodological frontier and future directions

Evaluation of sustainable transport research in 2000–2019

The asymmetric effect of public private partnership investment on transport CO2 emission in China: Evidence from quantile ARDL approach

Effective incident management system requires quantifying non-recurring congestion and detecting a secondary incident under the negative influence of a primary incident. Previously suggested thresholds and measurement parameters provide no universal definition of a secondary incident, regardless of discussions on the topic. To solve this dilemma, we propose a Bayesian structure equation model to recognize congestion patterns for road segments using INRIX Data. An adjustment of the boxplot is applied to capture segments at the tail of the queue and at the head of the queue where secondary incidents might occur. The resulting contour plot provides temporal–spatial area under congestion to identify secondary incidents. The likelihood of classified secondary incidents are sequentially predicted from the point of incident response to the road clearance. The prediction performance of the principled Bayesian learning approach to neural networks outperforms the logistic model. The quality of predictions improve as new information (e.g. notification-arrival of response units, speed) becomes available. A pedagogical rule extraction approach will improve the ability to understand secondary incidents by extracting comprehensible rules from the neural networks. The symbolic description represents a series of decisions to assist emergency operators in their decision-making capabilities.

Real-time prediction of secondary incident occurrences using vehicle probe data

https://www.rosemonteis.us/files/references/045469.pdf

Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression.

Gaussian Mixture Model-Based Speed Estimation and Vehicle Classification Using Single-Loop Measurements

Releaving traffic congestion by developing public transport as an alternative mode of travel is a common practice all over the world. However, the increasing public transport subsidies have created a financial burden for governments. Encouragingly, private capital supplies an opportunity for public transport in sustainable finance. Previous research mainly focuses on qualitative analysis and money-for-value (MFV) analysis. In this paper, a new investment model is proposed based on the concept ‘passenger value’, and a bi-level programming model (BLPM) is constructed as a quantitative analysis tool. The upper target of BLPM is the total surplus (including the value of time (VOT) of passengers) of the public transport system and the upper constraint is the ticket price. The lower target of BLPM is passenger’s surplus, the lower constraints are service capability and the lowest return rate of the private sector. The public transport of Jinan City, China is taken as a case to quantify the impacts of private capital investment in public transport. Results show that the proposed investment model considering passenger value is superior to the traditional one, and effective private capital investment could increase the total societal benefit of the transportation system. The proposed investment strategy satisfies economic viability and is a financially sustainability strategy. Additionally, travelers should be encouraged to use public transport through improving the service quality and passenger returns. Only in this way can the success rate of the private sector investment in public transport be improved efficiently.

Quantifying a Financially Sustainable Strategy of Public Transport: Private Capital Investment Considering Passenger Value

Because access to travel time information can significantly influence the decision making of both agencies and travelers, accurate and reliable travel time information is increasingly needed. One important step in providing that information is to identify the sensors best suited to provide travel time data for a given corridor. Currently, few studies have evaluated the effectiveness of various travel time data collection technologies side-by-side. This evaluation was intended to provide decision support for transportation agencies looking to select travel time systems on the basis of accuracy, reliability, and cost. This study focused on two test corridors: State Route 522 (SR 522) (an urban arterial with frequent intersections) between the NE 153rd Street and 83rd Place NE intersections, and I-90 (rural freeway built over Snoqualmie Pass in the Cascade mountains) from milepost 109 (Ellensburg, Washington) to milepost 32 (North Bend, Wash). The sensor systems tested were Washington State Department of Transportation’s pre-existing automatic license plate reader (ALPR) system, Sensys emplacements, the TrafficCast BlueTOAD system, Blip Systems BlipTrack sensors, and a third-party feed from Inrix. This study’s approach was to look at the Mean Absolute Deviation (MAD) to judge the expected magnitude of error, then examine the Mean Percent Error (MPE) to find any systematic biases in the data. The Mean Absolute Percent Error (MAPE) was useful for finding the relative magnitude of the error, and the Root Mean Square Error (RMSE) was used to determine whether a few large errors or many smaller errors were occurring. Each system in the analysis demonstrated different strengths and weaknesses that should be considered in addition to its accuracy and sample rates. Some systems can provide additional data; others trade accuracy and coverage for cost or portability. Ultimately, engineers will need to weigh their requirements for accuracy and sample rates against the other engineering constraints imposed on their system.

https://rosap.ntl.bts.gov/view/dot/27777/dot_27777_DS1.pdf

Error Assessment for Emerging Traffic Data Collection Devices

To address the previous researches’ shortcomings on private capital investment in public transportation, a new private capital investment method in public transportation considering passenger value is proposed. An Evolutionary Game Model (EGM) is constructed to quantify the impacts of private capital investment. Evolutionary Game Theory (EGT) supposes players are bounded rational; therefore EGM is more in accordance with the real situation. Jinan city is detailed as a case study to show how public and private sectors change their strategies. Model results show that the proposed investment strategy is superior to the traditional one and is evolutionary stable, thus the strategy should be encouraged. The EGM established in this paper provides a quantitative analysis tool for administrators and researchers to guide investment strategies of private capital in public transportation and lays the foundation for further research.

Jonathan Corey

Papers

Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression.

Gaussian Mixture Model-Based Speed Estimation and Vehicle Classification Using Single-Loop Measurements

Quantifying a Financially Sustainable Strategy of Public Transport: Private Capital Investment Considering Passenger Value

Error Assessment for Emerging Traffic Data Collection Devices

Evolutionary Game Model of Private Capital Investment in Urban Public Transportation