Developing a Travel Time Congestion Index

Levinson, Herbert S; Lomax, T J

doi:10.1177/0361198196156400101

Cited by 35 publications

(25 citation statements)

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“…This is an important question because spatiotemporal variabilities in traffic congestion intensities may exacerbate the UGCoP. Although it is widely known that traffic congestion intensities are different over space and time [55], here we empirically investigate its precise spatiotemporal configurations in the study area.…”

Section: Resultsmentioning

confidence: 99%

Beyond Commuting: Ignoring Individuals’ Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion

Kim

Kwan

2018

IJERPH

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This research examines whether individual exposures to traffic congestion are significantly different between assessments obtained with and without considering individuals’ activity-travel patterns in addition to commuting trips. We used crowdsourced real-time traffic congestion data and the activity-travel data of 250 individuals in Los Angeles to compare these two assessments of individual exposures to traffic congestion. The results revealed that individual exposures to traffic congestion are significantly underestimated when their activity-travel patterns are ignored, which has been postulated as a manifestation of the uncertain geographic context problem (UGCoP). The results also highlighted that the probability distribution function of exposures is heavily skewed but tends to converge to its average when individuals’ activity-travel patterns are considered when compared to one obtained when those patterns are not considered, which indicates the existence of the neighborhood effect averaging problem (NEAP). Lastly, space-time visualizations of individual exposures illustrated that people’s exposures to traffic congestion vary significantly even if they live at the same residential location due to their idiosyncratic activity-travel patterns. The results corroborate the claims in previous studies that using data aggregated over areas (e.g., census tracts) or focusing only on commuting trips (and thus ignoring individuals’ activity-travel patterns) may lead to erroneous assessments of individual exposures to traffic congestion or other environmental influences.

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Section: Resultsmentioning

confidence: 99%

Beyond Commuting: Ignoring Individuals’ Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion

Kim

Kwan

2018

IJERPH

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“…We measure Congestion Index (CI) as the proportion of travel time lost in congestion based on the traffic speed information considering some of the desirable attributes provided by Levinson and Lomax (1996), Boarnet et al (1998) and Lomax et al (1997). According to these researchers, a congestion index/metric should (i) be easy to communicate, (ii) measure congestion at network level, (iii) be based on widely available data, (iv) allow comparison across metropolitan cities (v) have statistical analysis capability and (vi) provide a continuous range of values [ 3 , 33 , 34 ]. Lyman and Bertini (2008) defined CI as the ratio of travel time required during peak traffic periods to the normal travel time [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Characterizing multicity urban traffic conditions using crowdsourced data

et al. 2019

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Road traffic congestion continues to manifest and propagate in cities around the world. The recent technological advancements in intelligent traveler information have a strong influence on the route choice behavior of drivers by enabling them to be more flexible in selecting their routes. Measuring traffic congestion in a city, understanding its spatial dispersion, and investigating whether the congestion patterns are stable (temporally, such as on a day-to-day basis) are critical to developing effective traffic management strategies. In this study, with the help of Google Maps API, we gather traffic speed data of 29 cities across the world over a 40-day period. We present generalized congestion and network stability metrics to compare congestion levels between these cities. We find that (a) traffic congestion is related to macroeconomic characteristics such as per capita income and population density of these cities, (b) congestion patterns are mostly stable on a day-to-day basis, and (c) the rate of spatial dispersion of congestion is smaller in congested cities, i.e. the spatial heterogeneity is less sensitive to increase in delays. This study compares the traffic conditions across global cities on a common datum using crowdsourced data which is becoming readily available for research purposes. This information can potentially assist practitioners to tailor macroscopic network congestion and reliability management policies. The comparison of different cities can also lead to benchmarking and standardization of the policies that have been used to date.

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“…As previously described in literature [82,101], free flow is characterized by an accepted delay. This means that even in free flow, the maximum speeds allowed will mostly not be reached.…”

Section: Discussionmentioning

confidence: 97%

“…After determining the detour factor regression, the API can be used to determine the average speed during free-flow state. The free-flow state describes the traffic flow without congestion exceeding an agreed upon norm [101]. This means that delays due to infrastructural influences such as speed limits or traffic light changes are considered part of the free flow.…”

Section: Infrastructurementioning

confidence: 99%

Towards Sustainable Cities: Utilizing Floating Car Data to Support Location-Based Road Network Performance Measurements

2020

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Road network performance (RNP) is a key element for urban sustainability as it has a significant impact on economy, environment, and society. Poor RNP can lead to traffic congestion, which can lead to higher transportation costs, more pollution and health issues regarding the urban population. To evaluate the effects of the RNP, the involved stakeholders need a real-world data base to work with. This paper develops a data collection approach to enable location-based RNP analysis using publicly available traffic information. Therefore, we use reachable range requests implemented by navigation service providers to retrieve travel times, travel speeds, and traffic conditions. To demonstrate the practicability of the proposed methodology, a comparison of four German cities is made, considering the network characteristics with respect to detours, infrastructure, and traffic congestion. The results are combined with cost rates to compare the economical dimension of sustainability of the chosen cities. Our results show that digitization eases the assessment of traffic data and that a combination of several indicators must be considered depending on the relevant sustainability dimension decisions are made from.

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Developing a Travel Time Congestion Index

Cited by 35 publications

References 0 publications

Beyond Commuting: Ignoring Individuals’ Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion

Beyond Commuting: Ignoring Individuals’ Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion

Characterizing multicity urban traffic conditions using crowdsourced data

Towards Sustainable Cities: Utilizing Floating Car Data to Support Location-Based Road Network Performance Measurements

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