Truck acceleration behavior study and acceleration lane length recommendations for metered on-ramps

Yang, Guangchuan; Xu, Hao; Wang, Zhongren; Tian, Zong

doi:10.1016/j.ijtst.2016.09.006

Cited by 45 publications

(27 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Second, while a truck may involve acceleration/deceleration when switching from one road segment to another road segment, the acceleration/deceleration distance is negligible as compared to the length of road segments. For example, as shown in [46], the heavy-duty truck can accelerate from zero speed to 31mph in just 500 feet, while the average length of highway road segments is 3.26 miles, according to our study of the U.S. national highway data (see Tab. IV).…”

Section: A System Modelmentioning

confidence: 78%

Energy-Efficient Timely Transportation of Long-Haul Heavy-Duty Trucks

Deng

Hajiesmaili

Chen

et al. 2018

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

We consider a timely transportation problem where a heavy-duty truck travels between two locations across the national highway system, subject to a hard deadline constraint. Our objective is to minimize the total fuel consumption of the truck, by optimizing both route planning and speed planning. The problem is important for cost-effective and environment-friendly truck operation, and it is uniquely challenging due to its combinatorial nature as well as the need of considering hard deadline constraint. We first show that the problem is NP-complete; thus exact solution is computational prohibited unless P = NP. We then design a fully polynomial time approximation scheme (FPTAS) to solve it. While achieving highly-preferred theoretical performance guarantee, the proposed FPTAS still suffers from long running time when applying to national-wide highway systems with tens of thousands of nodes and edges. Leveraging elegant insights from studying the dual of the original problem, we design a heuristic with much lower complexity. The proposed heuristic allows us to tackle the energy-efficient timely transportation problem on large-scale national highway systems. We further characterize a condition under which our heuristic generates an optimal solution. We observe that the condition holds in most of practical instances in numerical experiments, justifying the superior empirical performance of our heuristic. We carry out extensive numerical experiments using real-world truck data over the actual U.S. highway network. The results show that our proposed solutions achieve 17% (resp. 14%) fuel consumption reduction, as compared with a fastest path (resp. shortest path) algorithm adapted from common practice.

show abstract

Section: A System Modelmentioning

confidence: 78%

Energy-Efficient Timely Transportation of Long-Haul Heavy-Duty Trucks

Deng

Hajiesmaili

Chen

et al. 2018

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Based on the observation and video recording, this paper deployed the default truck percentage setting in VISSIM (2%). For model calibration of truck behavior, this paper employed the speed and acceleration data recommended by Yang et al [28,29]. For passenger vehicle driver behaviors, apart from vehicle length and standstill distance, the vehicle speed was majorly concerned in this study.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Microsimulation Analysis of Traffic Operations at Two Diamond Interchange Types

Yue

Yang

Tian

et al. 2019

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

The operational performance of standard Single Point Urban Interchange (SPUI) and Tight Diamond Interchange (TDI) has already been widely studied. In general, SPUI is more efficient than a TDI in terms of increased capacity and decreased traffic delays even though building a SPUI generally incurs a higher cost. However, due to right-of-way constraints, a standard SPUI may not be implementable at locations with restricted land use; thus, the variations of SPUI are usually considered. Currently, there is no established methodology or guideline available on the performance evaluation of variations of SPUI. This paper aims to investigate the operational efficiency of SPUI with frontage roads (SPUI-F, a variation of SPUI). Based on a field case, the performance of SPUI-F was investigated using microsimulation. An analytical model for capacity estimation, which considered early return and discharge flow rate, was also established and validated based on microsimulation. Multiple traffic scenarios were analyzed and their performance measures were compared against the equivalent TDI design. Simulation results revealed that TDI outperformed SPUI-F in terms of average delay, speed, and queue length, and the proposed analytical model can be used for reliable capacity and delay estimation. The findings from this study can aid the decision-makers choosing an appropriate interchange type for achieving the best benefit-cost ratio.

show abstract

“…To simulate the driver's behavior in the adjustment section, the deceleration model needs the reference value to follow. To find the appropriate equation to be used as reference acceleration, the constant acceleration (CA) model, which is widely used in speed trajectory prediction, was used [17][18][19][20][21]. The CA model is a parametric equation to calculate the acceleration in order to reach a desirable velocity at a specific location, as shown in Equation 7, where v 2 is the desirable velocity, v 1 is the current vehicle speed, and d is the distance to the specific location.…”

Section: Adjustment Sectionmentioning

confidence: 99%

“…Calculate the initial index from the driving data; 2. Generate the Gaussian distribution, which has the mean value of the initial index calculated at step 1, as shown in Figure 10, using Equation (18), where is the value and is the standard deviation of the initial index; The major role of the learning vector is managing driver characteristics, but there is also one more important thing that it performs. The values of driver parameters used in the deceleration model are calculated based on the learning vector.…”

Section: Learning and Effective Probability Vectorsmentioning

confidence: 99%

Automatic Longitudinal Regenerative Control of EVs Based on a Driver Characteristics-Oriented Deceleration Model

Sim

Ahn

Park

et al. 2019

WEVJ

View full text Add to dashboard Cite

To preserve the fun of driving and enhance driving convenience, a smart regenerative braking system (SRS) is developed. The SRS provides automatic regeneration that is appropriate for the driving conditions, but the existing technology has a low level of acceptability and comfort. To solve this problem, this paper presents an automatic regenerative control system based on a deceleration model that reflects the driver’s characteristics. The deceleration model is designed as a parametric model that mimics the driver’s behavior. In addition, it consists of parameters that represent the driver’s characteristics. These parameters are updated online by a learning algorithm. The validation results of the vehicle testing show that the vehicle maintained a safe distance from the leading car while simulating a driver’s behavior. Of all the deceleration that occurred during the testing, 92% was conducted by the automatic regeneration system. In addition, the results of the online learning algorithm are different based on the driver’s deceleration pattern. The presented automatic regenerative control system can be safely used in diverse car-following situations. Moreover, the system’s acceptability is improved by updating the driver characteristics. In the future, the algorithm will be extended for use in more diverse deceleration situations by using intelligent transportation system information.

show abstract

Truck acceleration behavior study and acceleration lane length recommendations for metered on-ramps

Cited by 45 publications

References 4 publications

Energy-Efficient Timely Transportation of Long-Haul Heavy-Duty Trucks

Energy-Efficient Timely Transportation of Long-Haul Heavy-Duty Trucks

Microsimulation Analysis of Traffic Operations at Two Diamond Interchange Types

Automatic Longitudinal Regenerative Control of EVs Based on a Driver Characteristics-Oriented Deceleration Model

Contact Info

Product

Resources

About