Spatiotemporal clustering in urban transportation

Abstract

Public buses are an important part of the urban transportation mix. However, a considerable disadvantage of buses is their slow speed, which is in part due to frequent stops, but also due to the lack of segregation from other vehicles in traffic. As such, assessing bus routes and the respective sections that are prone to congestion is an important aspect of route planning, scheduling, and the creation of dedicated bus lanes. In this work we use bus tracking data from the Washington Metropolitan Area Transit Authority, to discover speed patterns of specific bus routes in relation to the road network throughout the day. Specifically, we focus on using these patterns to identify free flow segments, bus stop locations, traffic light locations and road segments prone to congestion.

Exploiting Multi-modal Contextual Sensing for City-bus's Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction

“…Among these, a good number of works have focused on identification and characterization of various point of interests over a route, which can impact the mobility of the vehicles, such as point of intersections [5], various road attributes [13,40,42,47], mobility patterns [24], traffic congestion [14,19,29], significant locations on a route [23], and so on. A handful of these works also consider intra-city and public bus travels [12,13,19,23,24,30], where the GPS data has been collected either through a vehicle-mounted sensor or from smartphone crowdsensing. In this section, we have presented a comprehensive study of related works in two directions.…”

“…They are interested in a specific type of PoI, i.e., charging station. In their works, Fei et al [12,13] have used odometer data to infer the impact of various stay locations, like bus-stops, signals, congestion, etc. on the bus routes.…”

Spatiotemporal Bus Route Profiling using Odometer Data

“…Wang et al [22] clustered bus stops according to the number of passengers who boarded, but did not consider traffic conditions. Fei et al [7] used speed patterns of bus routes from AVL to identify different categories of route segments which include bus stop locations. This preliminary approach prescribes first ideas to analyze the traffic at bus stops, but does not have ground-truth data to evaluate their results.…”

“…The blue arrows below each column point to the actual location of the corresponding bus stop. The travel direction is from east to west, thus the numbering of stops is ordered from right to left (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). For comparison, we also present the mean bus speed values per (bus stop, trip) pair in Figure 8.…”