Jacob Holden

Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.

Green routing fuel saving opportunity assessment: A case study using large-scale real-world travel data

New technologies such as connected and automated vehicles have attracted more and more research attention for their potential to improve the energy efficiency and environmental impact of current transportation systems. Green routing is one such connected vehicle strategy under which drivers receive information about the most fuel-efficient route before departing for a given destination. This paper introduces an evaluation framework for estimating the benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption on alternate routes that could be taken between two locations and comparing these to the estimated fuel consumption of the actual route taken. A route-based fuel consumption estimation model that considers road traffic conditions, functional class, and grade is proposed and used in the framework. A study using a large-scale, high-resolution data set from the California Household Travel Survey indicates that 31% of actual routes have fuel savings potential, and among these routes the cumulative fuel savings could reach 12%. Alternately calculating the potential fuel savings relative to the full set of actual routes (including those that already follow the greenest route recommendation), the potential savings relative to the overall estimated fuel consumption would be 4.5%. Notably, two thirds of the fuel savings occur on green routes that save both fuel and time relative to the original actual routes. The remaining third would be subject to weighing the potential fuel savings against required increases in travel time for the recommended green route.

Navigation Application Programming Interface Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles:

The green-routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation application programming interface (API), route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). Navigation APIs, such as Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin–destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption models are built by assigning travel from many global positioning system driving traces to the links in TomTom MultiNet and road grade data from the U.S. Geological Survey elevation data set. Fuel consumption on a link is computed by the proposed model. This paper envisions two kinds of applications: (1) identifying alternate routes that save fuel, and (2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey global positioning system trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and travel time due to choosing different routes is also examined for both powertrains.

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin–destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

Exploring Telematics Big Data for Truck Platooning Opportunities

NREL completed a temporal and geospatial analysis of telematics data to estimate the fraction of platoonable miles traveled by class 8 tractor trailers currently in operation. This paper discusses the value and limitations of very large but low time-resolution data sets, and the fuel consumption reduction opportunities from large scale adoption of platooning technology for class 8 highway vehicles in the US based on telematics data. The telematics data set consist of about 57,000 unique vehicles traveling over 210 million miles combined during a two-week period. 75% of the total fuel consumption result from vehicles operating in top gear, suggesting heavy highway utilization. The data is at a one-hour resolution, resulting in a significant fraction of data be uncategorizable, yet significant value can still be extracted from the remaining data. Multiple analysis methods to estimate platoonable miles are discussed. Results indicate that 63% of total miles driven at known hourly-average speeds happens at speeds amenable to platooning. When also considering availability of nearby partner vehicles, results indicate 55.7% of all classifiable miles driven were platoonable. Analysis also address the availability of numerous partners enabling platoons greater than 2 trucks and the percentage of trucks that would be required to be equipped with platooning equipment to realize more than 50% of the possible savings.

Potentials for Platooning in U.S. Highway Freight Transport

Smart technologies enabling connection among vehicles and between vehicles and infrastructure as well as vehicle automation to assist human operators are receiving significant attention as a means for improving road transportation systems by reducing fuel consumption – and related emissions – while also providing additional benefits through improving overall traffic safety and efficiency. For truck applications, which are currently responsible for nearly three-quarters of the total U.S. freight energy use and greenhouse gas (GHG) emissions, platooning has been identified as an early feature for connected and automated vehicles (CAVs) that could provide significant fuel savings and improved traffic safety and efficiency without radical design or technology changes compared to existing vehicles. A statistical analysis was performed based on a large collection of real-world U.S. truck usage data to estimate the fraction of total miles that are technically suitable for platooning. In particular, our analysis focuses on estimating “platoonable” mileage based on overall highway vehicle use and prolonged high-velocity traveling, and established that about 65% of the total miles driven by combination trucks from this data sample could be driven in platoon formation, leading to a 4% reduction in total truck fuel consumption. This technical potential for “platoonable” miles in the United States provides an upper bound for scenario analysis considering fleet willingness and convenience to platoon as an estimate of overall benefits of early adoption of connected and automated vehicle technologies. A benefit analysis is proposed to assess the overall potential for energy savings and emissions mitigation by widespread implementation of highway platooning for trucks.