1,721,063 research outputs found
Component-level analysis for developing an energy consumption model for battery electric vehicles (BEVs) in operation
No full textIn battery electric vehicles (BEV), energy originates in the battery and is transmitted to the wheels through a series of energy conversion processes involving the inverter and motor. Therefore, understanding the energy conversion mechanisms in both the inverter and motor is essential for accurately modeling energy consumption. However, in previous studies, real-world driving data are often limited, making it challenging to fully analyze the complex and nonlinear relationships within each conversion component. In this study, we collected input-output data from the inverters and motors of fifty-four BEVs, measured repeatedly over time. The data revealed a piecewise nonlinear relationship between input and output, prompting us to partition the models by different phases: propulsion, regeneration, and battery status. For each phase, we applied linear mixed-effects models to account for the hierarchical structure of the data, estimating coefficients separately for the inverter and motor using a randomly selected 75% of the dataset. Through this component-level modeling approach, the models not only capture component-level random-effect parameters but also effectively model the nonlinear energy conversion characteristics at the component level. The two models were then integrated to estimate the total driving energy consumption of the BEVs, and the results were validated against actual observations using the total driving energy from the remaining 25% of the dataset. Model performance was evaluated using the Total Consumption Estimation Rate (TCER) and Mean Absolute Percentage Error (MAPE). The proposed model achieved at least 95.27% in TCER and 86.34% in MAPE, outperforming existing approaches with a 20% higher TCER and an MAPE approximately ten times lower on average. The comparison demonstrated that our model accurately estimates driving energy consumption, as it effectively captured the heterogeneous and nonlinear relationships between input and output energy for each component.
Regularity of vehicle trips in urban areas
No full textThe regularity of trips has been a fundamental assumption for estimating or forecasting travel demand, which is an essential part of the analysis for traffic operation and planning. For this type of analysis, a survey method has been traditionally used to understand personal trip behavior in detail. However, surveys have limitations in collecting accurate traffic data on a large scale because they depend purely on participants' memories. Recently, as the development of ICT facilitates collection of various traffic information, research using this new information has drawn attention, which discovers human mobility pattern on a wide range. In this study, a month of trajectory data collected from on-board transponders equipped in the vehicles in Daegu metropolitan city, South Korea was used to unveil the individual trip regularity in trip chain level. We applied dynamic time warping and the inter-spike interval algorithms to examine these data and to quantitatively measure spatial and temporal regularity separately. The outcomes showed that i) the degree of trip regularity can be properly estimated using the indices, ii) spatial and temporal regularities are correlated - drivers who made trips at regular times also used similar paths in space across days, and iii) commuters and non-commuters have different distributions of regularity scores - commuters made more regular trips. This finding is intriguing because the trip regularity can prove the predictability of human mobility. In addition, if regularity indices are used to measure historically collected trip behavior, this method can provide an alternative way of estimating or forecasting travel demand at the individual trip level
Experimental study on the temperature distribution and the heat flux insice the variable-speed scroll compressor
No full textEffect of Pedestrians on Right-Turn Capacity under Heterogeneous Traffic Conditions
No full textUnder right-hand traffic conditions, Pedestrians significantly affect vehicle right-turn capacity, as right-turn vehicle movements typically interact with opposing pedestrian flows. Despite substantial efforts over several decades to account for this effect in right-turn capacity models, current models may lack scalability when addressing multiple possible scenarios. In this study, a general model of the effect of pedestrians on right-turn capacity is developed by theoretically deriving the unblocked time for vehicles during the green phase. The proposed model considers variables such as pedestrian volumes and signal timings, as in several existing models, but also incorporates additional factors, including: 1) the length of the conflict zone, 2) the length and width of the crosswalk, 3) pedestrian types and their respective speeds, and 4) the types of right-turning vehicle. The model is validated under realistic conditions that take into account, for instance: 1) changes in right-turn policies, 2) unbalanced pedestrian volumes by direction, 3) mixed pedestrian traffic, and 4) mixed traffic conditions, along with signal timing and geometric variables as addressed in previous research, through comparisons with a microsimulation. Additionally, comprehensive evaluations are performed by comparing field measurements. Given its ability to account for a wider range of variables, the proposed model is more adaptable to diverse contexts than existing models. Furthermore, these characteristics make the model applicable in future autonomous vehicle environments.
The effects of rainfall on driving behaviors based on driving volatility
No full textThis research was an investigation of changes in driving behavior that occurs in response to rainfall intensity, especially focusing on risky behaviors. This was done using driving records of 620 taxis in Seoul (South Korea). We utilized driving volatility as a quantitative measure of driving behavior. This parameter indicates the variability of vehicle movement as indicated by vehicular acceleration and jerk. The result verified that, as the rainfall intensity increases, driving patterns deviate more from those without rainfall. From these changes, a measure of aggressiveness was derived considering these behavioral differences under different rainfall conditions. In particular, volatile and risky driving decisions with respect to jerk occur more frequently as rainfall intensity increases. This implies that changes in acceleration (e.g., acceleration after deceleration, deceleration after acceleration) are prevalent in rainy days. Furthermore, using crashes and law violation information about taxis, this research verified that higher volatility is related to a higher likelihood of crashes and law violations. The contributions of this study are that it quantifies the aggressiveness of drivers as a reflection of changing driving behavior under different rainfall conditions, and verifies the volatility index by relating to crashes and traffic law violations of individual drivers.
Evaluating the effectiveness of the law banning handheld cellphone use while driving
No full textCellphone use while driving is one of the major concerns in traffic safety, and numerous researches found that cellphone use while driving can increase traffic collision risk. With growing popularity of cellphone use, many states in the United States enacted the law banning handheld cellphone use while driving in recent years. However, there is a debate on effectiveness of the law. In this study, we analyze the six-year collision data between 2006 and 2010 in the state of California to examine the timing of a significant change in the trend of cellphone related collisions. We adopt the turning point analysis technique without imposing a prior belief on whether or when such a change has occurred. Both the frequentist and Bayesian approaches are applied to four different groups, including the all cellphone collision group and three subgroups characterized by cellphone usage. The result shows that the turning points coincides with the timing of enforcement of the handheld law in California, except for the subgroup containing hands-free related collisions only. We applied the same method to two confounding factors including driving under influence and CD/radio use, and find that the turning points do not agree with the cellphone related collisions. Although a more comprehensive set of confounding factors needs to be considered to establish a causal relationship between the handheld law and cellphone related collisions, coincidence between the handheld law and the turning point suggests that the law should be considered as one of the primary factors in cellphone related collisions reductio
High-Occupancy Vehicle Lane Configurations and Safety Performance of California Freeways: Investigation of Differential Distributions and Statistical Analysis
No full textFrom a recent study of safety evaluation of HOV-equipped freeways, it was found that limited-access HOV lanes appear to have a safety performance disadvantage when measured by collision distribution or collision rates for the HOV lane alone and for the HOV and left lanes combined. This paper describes the work performed to verify the statistical significance of related findings. Several statistical tests were used: empirical cumulative density function (CDF), Kolmogorov-Smirnov Tests, and comparison of means based on Poisson Distributed Samples. The conclusion that continuous-access HOV lanes perform better than limited-access ones by several safety metrics is confirmed by the three separate approaches. In addition, the historical data for the HOV segments and the general-purpose lanes are extracted and compared, which offers supporting evidence for similar conclusions. The work described in this paper offers a methodology of statistical verification and can provide support to assist policy-making in selecting HOV configurations
Demand Shifts and Their Effects on Traffic Congestion due to Toll Changes on Bridges in San Francisco Area
No full textIn the San Francisco Bay Area, tolls are collected from seven state-owned bridges (as shown in Figure 1 below). Over the last decades, the toll schedule and price have been changed at multiple instances to administer the operation of bridges, to finance several bridge maintenance projects and to manage the level of congestion. This poster explore changes in traffic demand, as a consequence of toll changes and the price elasticity of demand; shifts by mode, time of day and locations; congestion mitigation impacts; an evaluation near-term long-term effects
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