PDF(Pubmed)
Abstract：
Minibus taxis, a form of informal shared mobility that carries up to 16 passengers, is the main mode of public transport in sub-Saharan Africa, and given global trends, a large-scale shift to electric paratransit is imminent in the coming decades. Modeling the energy consumption (kWh/km) of electric vehicle (EV) fleets is a pre-requisite for planning for fleet deployment, especially in energy-constrained contexts. Given the paucity of EVs in sub-Saharan Africa, ground-truth data on the energy consumption of electric paratransit does not exist for many developing contexts. Consequently, GPS tracking data on internal combustion engine (ICE) versions of these vehicles is often used to estimate the energy consumption of an electric equivalent. To date, only per-minute GPS tracking data has been captured on these vehicles and used for energy consumption estimates. But this sampling frequency is insufficient for accurate energy consumption estimates, especially given the unique micro-mobility patterns of minibus taxis that are characterized by many rapid acceleration/deceleration events in quick succession. Although simulators can be used to interpolate between the dataset, they have been shown to be inaccurate in the regional context. This article presents a dataset of high-fidelity micromobility data captured on minibus taxis in transit on four typical route types: inter-city, intra-city, uphill, and downhill. The main objective was to estimate energy requirements for the eventual electrification of these vehicles, the data was thus processed according to an electro-kinetic model. This high-fidelity mobility data was captured by \"standardised passengers\" with bespoke GPS-location logging devices sampling at 1 Hz. Trips on the four route types were recorded and saved in six folders - three routes, each in two directions, with one route being uphill in one direction and downhill in another. Each of the six folders have subfolders for time of day - morning, afternoon, and evening. In total 62 trips were recorded with varying durations, depending on the traffic and route length.
摘要：
小巴出租车，一种非正式的共享出行方式，最多可搭载16名乘客，是撒哈拉以南非洲的主要公共交通方式，鉴于全球趋势，在未来几十年里，电力辅助运输的大规模转移迫在眉睫。对电动汽车（EV）车队的能耗（kWh/km）进行建模是规划车队部署的先决条件，尤其是在能量受限的情况下。鉴于撒哈拉以南非洲电动汽车的匮乏，对于许多发展中的环境，不存在关于电力辅助运行能耗的地面实况数据。因此,这些车辆的内燃机(ICE)版本的GPS跟踪数据通常用于估计电动等效物的能量消耗。迄今为止,这些车辆上只有每分钟的GPS跟踪数据被捕获并用于能耗估算。但是这种采样频率不足以进行准确的能耗估算，特别是考虑到小巴出租车独特的微移动模式，其特点是快速连续发生许多快速加速/减速事件。尽管模拟器可用于在数据集之间进行插值，在区域背景下，它们被证明是不准确的。本文介绍了在四种典型路线类型上的小巴出租车上捕获的高保真微移动性数据集：城际，城市内部，上坡,下坡。主要目的是估计这些车辆最终电气化的能量需求，因此，数据根据电动力学模型进行处理。这种高保真移动性数据是由“标准化乘客”使用定制的GPS位置记录设备以1Hz的频率采样捕获的。四种路线类型的行程被记录并保存在六个文件夹中-三条路线，每个方向都有两个方向，一条路线在一个方向上坡，在另一个方向下坡。六个文件夹中的每一个都有一天早上的子文件夹，下午,和晚上。总共记录了62次不同持续时间的行程，取决于交通和路线长度。
