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Investigation the Effect of the Data Frequency on the Driving Cycle of an Urban Bus Route
Abstract
In this paper, focusing on a designated route of public transport in Debrecen, we examine the possibilities of creating a driving cycle representative for the route. The background of the work is, that in the framework of a larger-scale research project we examine the possibility of how to reduce the bus’s emissions by modifying the drive chain of the current diesel vehicle. A dynamic model suitable for performing the calculations was developed, in which the movement of the vehicle in traffic is described by the driving cycle. The data provided by the data acquisition equipment of the bus on the designated route is available for the creation of the special driving cycle. In this paper, we look for the answer to the question: how the difference in data density affects the driving cycle and how it modifies the parameters describing its representativeness.
KeywordsReal traffic dataMicro-trip methodDriving cycleUrban bus route
... illustrates different drive cycles for New York, US [16], Xi'an, China [175], and Florence, Italy [176]. The comparison of these drive cycles is in Table 2. Additionally, certain studies estimate the characteristics of Pune, India [177], Beijing, China [178], and Debrecen, Hungary [179]. Figure 10. ...
Innovative technological solutions have become increasingly critical in addressing the transportation sector’s environmental impact. Passenger vehicles present an opportunity to introduce novel drivetrain solutions that can quickly penetrate the electric vehicle market due to their shorter development time and lifetime compared to commercial vehicles. As environmental policy pressure increases and customers demand more sustainable products, shifting from a linear business approach to a circular economy model is in prospect. The new generation of economically competitive machines must be designed with a restorative intention, considering future reuse, refurbishment, remanufacture, and recycling possibilities. This review investigates the market penetration possibilities of permanent magnet-assisted synchronous reluctance machines for mini and small-segment electric vehicles, considering the urban environment and sustainability aspects of the circular economy model. When making changes to the materials used in an electric machine, it is crucial to evaluate their potential impact on efficiency while keeping the environmental impact of those materials in mind. The indirect ecological effect of the vehicle’s use phase may outweigh the reduction in manufacturing and recycling at its end-of-life. Therefore, thoroughly analysing the materials used in the design process is necessary to ensure maximum efficiency while minimising the environmental impact.
... In recent years, the use of different big data sets, the modeling of passenger flow, and the creation of various emission models have become the most preferred approaches to estimate vehicle emissions under various traffic conditions, [2], [3], [4], [5], [6]. Optimization models and heuristic algorithms can be tested on different traffic networks, [7]. ...
In this paper, a multi-scale single-step future driving cycle prediction method is proposed. The driving cycle is discretized at given time scale, and the mesh accuracy of velocity and acceleration is determined to be 1Km/h and 0.05m/s² respectively, then Markov state transfer matrix can be obtained by categorizing these discretization points and statistical calculation. After that, future driving cycle prediction can be accomplished by proposed method which combines Markov chain and Monte Carlo method. Finally, Root-Mean-Square Deviation is introduced to assess the prediction accuracy. Comparing the prediction accuracy of multi-scale single-step method and traditional fix-scale multi-step method, it can be found that the prediction results of proposed method reach expectant accuracy improving by 7.18% on average.
Background: On-road vehicle emissions models couple emission rates with travel activities. Emission rates are derived from data collected using dynamometers. Dynamometer tests measure emissions while a vehicle follows a drive cycle with various speed and modal (acceleration, cruise, deceleration, idle) events. Existing cycle construction methods focus on representing real-world activity, without also directly weighing the relationship between activity and emissions. In addition, cycles have traditionally focused on representing complete trips, rather than activity on specific roadway links or road types. Methods: This paper presents a new cycle construction methodology that offers three advantages over traditional approaches. First, it creates cycles that represent statistically-defined speed classes; this groups activities into bins more closely associated with emissions. Second, it defines modal events using speed and event-duration criteria that improve representation of real-world activity. Third, it improves upon traditional approaches that create cycles primarily to match real-world Speed-Acceleration Frequency Distributions (SAFDs). The new method requires that cycles match real-world SAFDs and distributions of modal events. This work also introduces a new test statistic to assess cycle performance: the Composite Performance Measure (CPM). The CPM measures how well a cycle matches the SAFD and modal distributions of real-world data. We illustrate these new methods by creating arterial roadway cycles.
As the fundamental building block of the emissions estimation process, a driving cycle needs to be representative of real-world driving behavior. The driving cycle construction method becomes crucial for generating a representative driving cycle. In this paper, we revisit the Unified Cycle's (i.e., the LA92 driving cycle) construction method. The California Air Resources Board's Unified Cycle used a “microtrips” approach, a speed–acceleration frequency distribution plot, and a quasi-random selection mechanism to build the driving cycle. There is concern that the Unified Cycle does not reflect the true driving patterns due to the identified flaws in the construction methodology. Treating a driving trace as a stochastic process, we construct a new driving cycle (LA01) with the same driving data originally used to build the Unified Cycle. We then compare the two driving cycles with the sample data set with respect to the durations and intensities of the modal events. The new driving cycle is found to better replicate the modal events observed in the sample data. A comparison of average road power values between the sample data, LA01, and the Unified Cycle also confirms the effect of fine-scale driving on emissions. These differences result from the different construction approaches and can be expected to affect emissions inventory estimation.
In the literature it has been proven several times, that the common driving cycles are not accurate enough to estimate the fuel and energy consumption data or emission data for vehicles, especially for public transport vehicles, such as buses. One can find driving cycles for buses, but the special driving environment in Debrecen, Hungary cannot be accurately represented by them. As a part of a project focusing on the determination of fuel and energy consumption of vehicles by dynamic simulation, a study has been carried out for determining driving cycles, which are representative for Debrecen. In this paper the development of a driving cycle for city buses in Debrecen is presented. Typical bus routes were selected in the town for the study, the vehicles were instrumented with an appropriate data collecting system. These onboard units were used to collect data such as travelled distance, speed and acceleration on several vehicles in the real-world traffic. We used special purpose software to pre-process and filter the collected data. After pre-processing a statistical evaluation of the data was carried out. Using a version of the widely spread micro-trip method of driving cycle development, a Bus Driving Cycle has been constructed for Debrecen.
Autonóm járműirányítás esetén a trajektória meghatározása az egyik legfontosabb feladat. A pályagörbe meghatározása azonban jócskán túlmutat a technológiai feltételeken, hiszen az esetek többségében ezek a járművek embereket szállítanak, és emberi sofőrökkel irányított közlekedési rendszerben mozognak. Így fontos, hogy az emberi tűrőképességet, reakcióidőket, komfortzónákat is figyelembe vevő rendszert tudjunk megvalósítani. Az ehhez szükséges, jelenleg emberi sofőrök által vezetett járműveken végeztünk méréseket, amelyek segítenek a komfortzónák határait meghatározni.
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