Modeling Bus Dwell Time and Time Lost Serving Stop in China

“…A bus stop influence area includes three sections: bus entry area, alighting and boarding areas for passengers, and exit area. In general, a stopped bus will slow from its running speed about 50 m before the bus stop, and accelerate back to its running speed about 30 m after the stop [44,45]. Thus, in this study, such a range of areas was regarded as the bus stop influence area, as shown in Figure 2a.…”

Section: Resultsmentioning

confidence: 99%

On-Road Bus Emission Comparison for Diverse Locations and Fuel Types in Real-World Operation Conditions

Wang

Sun

2020

Sustainability

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Urban buses have energy and environmental impacts because they are mostly equipped with heavy-duty diesel engines, having higher emission factors and pollution levels. This study proposed a mean distribution deviation (MDD) method to identify bus pollutant emissions including CO, CO 2 , HC, and NO X at road sections, intersections, and bus stops for different fuel types; and explore the changes in emissions for different locations in the road sections, bus stops, and intersection influence areas. Bus speed, acceleration, and emissions data were collected from four fuel types in China. For different locations and fuel types, the differences in emissions were all statistically significant. MDD values for different locations indicated that there were more obvious differences in emissions between road sections and intersections. In addition, heat maps were applied in this study to better understand changes in bus emissions for different locations in the bus stop influence areas, intersection influence areas, and road sections. the proportion of new energy vehicles in buses is largely increasing, it is necessary to re-estimate the emissions characteristics of public transport vehicles, especially for new energy buses. In particular, intersection and bus stop areas can create congestion and further increase emission rates due to speed changes and stop-and-go operations [24][25][26][27]. In light of these considerations, measuring and comparing bus emissions at different locations are one of the emphases of this study.The first category of vehicle emission analysis methods is aimed at analyzing exhaust fumes for large areas and regions. This kind of emission analysis is regarded as a "macro" scale and is combined with the average values of influencing variables [28]. The second category aims to develop models to provide more precise knowledge about vehicle emissions in easily identifiable environments, which are called "micro" or "local" scale models. This approach offers the chance to acquire vehicle emissions data in real-world conditions [28,29]. Zhang et al. investigated the emission characteristics of transportation vehicles using real data [30]. From the results, the problem of NO X emissions is still confronted with severe challenges. For different locations, the performance of pollutant emissions may vary. In the relevant reference [18], approximately half of bus emissions were produced in and around intersection/stop areas. In addition, the use of alternative fuels, such as compressed natural gas, biodiesel, and hybrid technology (diesel/electric) reduced carbon emissions, leading to improved air quality [31][32][33][34]. For instance, Dreier et al. found an absolute reduction of 1115 g CO 2 per kilometer when using plug-in hybrid-electric city buses [35]. It should be noted that if the available data and information about the application system and technologies (such as bus emissions) are incomplete, imprecise, and uncertain, then technology identification could be very difficult. Liu et al. introduced a simpl...

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“…In our case we use the values a = 15 and b = 5 (seconds), which are consistent with those recommended in [2,12,16].…”

Section: Problem Definitionmentioning

confidence: 86%

The School Bus Routing Problem: An Analysis and Algorithm

Lewis

Smith‐Miles

Phillips³

2018

Lecture Notes in Computer Science

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In this paper we analyse a flexible real world-based model for designing school bus transit systems and note a number of parallels between this and other well-known combinatorial optimisation problems including the vehicle routing problem, the set covering problem, and one-dimensional bin packing. We then describe an iterated local search algorithm for this problem and demonstrate the sort of solutions that we can expect with different types of problem instance.

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Modeling Bus Dwell Time and Time Lost Serving Stop in China

Abstract: The

Cited by 31 publications

References 24 publications

A heuristic algorithm for finding cost-effective solutions to real-world school bus routing problems

A heuristic algorithm for finding cost-effective solutions to real-world school bus routing problems

On-Road Bus Emission Comparison for Diverse Locations and Fuel Types in Real-World Operation Conditions

The School Bus Routing Problem: An Analysis and Algorithm

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