scholarly journals A Comparative Assessment of Crowded Source Travel Time Estimates: A Case Study of Bluetooth vs INRIX on a Suburban Arterial

2014 ◽  
Author(s):  
Fahad Alhajri
Keyword(s):  
Travel Time ◽  
Comparative Assessment ◽  
Time Estimates

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

2021 ◽  
pp. 036119812110315
Author(s):  
Markus Steinmaßl ◽  
Stefan Kranzinger ◽  
Karl Rehrl
Keyword(s):  
Travel Time ◽  
Time Of Day ◽  
Temporal Aggregation ◽  
Travel Time Reliability ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Depth Analysis ◽  
Data Source ◽  
Travel Time Index

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

MEASURING SPATIAL HEALTHCARE FACILITIES ACCESSIBILITY USING TWO-STEP FLOATING CATCHMENT ANALYSIS (2SFCA) (Case Study: Cianjur Regency, Indonesia)

2021 ◽  
pp. 11
Author(s):  
Muhamad Iqbal Januadi Putra ◽  
Nabila Dety Novia Utami
Keyword(s):  
Travel Time ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Spatial Accessibility ◽  
Healthcare Facilities ◽  
Spatial Disparities ◽  
Time Threshold

The presence of healthcare facilities is quite essential to provide good healthcare services in a particular area, however, the existence of healthcare facilities is not evenly distributed in Cianjur Regency. This condition leads to the disparities of healthcare facilities across the Cianjur Regency. In this paper, we aim to measure and map the spatial disparities of healthcare facilities using a Two-Step Floating Catchment Analysis (2SFCA). This method can calculate the magnitude of spatial accessibility for healthcare facilities by formulating the travel time threshold and the quality of healthcare facilities across the study area. This research shows the result that the spatial accessibility of healthcare facilities in the Cianjur Regency is not evenly distributed across the districts. The spatial accessibility value resulted from 2SFCA is ranging from 0- 3.97. A low value indicates low spatial accessibility, while a higher value shows good accessibility. The majority of districts in the Cianjur Regency have the spatial accessibility value 0-0.5 (86%). Meanwhile, only a few have the higher value; value 0.5-0.99 as much as 6.6%, 0.99-1.49 as 3.3%, and 3.48-3.97 has a percentage of 3.3%. Also, this analysis results in the cluster of good spatial accessibility in healthcare facilities, namely the Pagelaran District and Cipanas District. Interestingly, the downtown of Cianjur Regency has lower spatial accessibility compared to both areas.

scholarly journals The Effect of Route-choice Strategy on Transit Travel Time Estimates

2019 ◽  
Author(s):  
Nate Wessel ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Imperfect Information ◽  
Optimal Path ◽  
Shortest Paths ◽  
Route Choice ◽  
Travel Times ◽  
Selection Strategies ◽  
Time Estimates ◽  
Average Travel Time

Estimates of travel time by public transit often rely on the calculation of a shortest-path between two points for a given departure time. Such shortest-paths are time-dependent and not always stable from one moment to the next. Given that actual transit passengers necessarily have imperfect information about the system, their route selection strategies are heuristic and cannot be expected to achieve optimal travel times for all possible departures. Thus an algorithm that returns optimal travel times at all moments will tend to underestimate real travel times all else being equal. While several researchers have noted this issue none have yet measured the extent of the problem. This study observes and measures this effect by contrasting two alternative heuristic routing strategies to a standard shortest-path calculation. The Toronto Transit Commission is used as a case study and we model actual transit operations for the agency over the course of a normal week with archived AVL data transformed into a retrospective GTFS dataset. Travel times are estimated using two alternative route-choice assumptions: 1) habitual selection of the itinerary with the best average travel time and 2) dynamic choice of the next-departing route in a predefined choice set. It is shown that most trips present passengers with a complex choice among competing itineraries and that the choice of itinerary at any given moment of departure may entail substantial travel time risk relative to the optimal outcome. In the context of accessibility modelling, where travel times are typically considered as a distribution, the optimal path method is observed in aggregate to underestimate travel time by about 3-4 minutes at the median and 6-7 minutes at the \nth{90} percentile for a typical trip.

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