Performance of transportation network under perturbations: Reliability, vulnerability, and resilience

Yu, Gu; Fu, Xiao; Liu, Zhiyuan; Xu, Xiangdong; Chen, Anthony

doi:10.1016/j.tre.2019.11.003

Cited by 196 publications

(98 citation statements)

References 84 publications

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“…the variation of the delays) is remarkably smaller during the disruption period. A reduced performance has been linked to a reduced variability of performance during a disruption also in other cases 5 .…”

Section: Resultsmentioning

confidence: 99%

“…The quantification of the vulnerability of transport networks has been attracting much interest 3 – 5 . In topological studies of link or node failures, network-based properties are predominant 6 – 9 .…”

Section: Introductionmentioning

confidence: 99%

“…controlled experiment), which is very rare in reality. Moreover, railway disruptions have been subject to limited study 3 , 5 , 22 , 41 probably due to exactly those complex characteristics of delay propagation, and their interplay with management actions aiming to keep the system running 31 , often taken under strong time pressure and resulting suboptimal in hindsight 17 , 19 , 42 .

Figure 1 Graphical representation of Switzerland, boundary points considered, disruption location and schematic traffic entering Switzerland from Germany (left: undisrupted case; right: disrupted case.…”

Section: Introductionmentioning

confidence: 99%

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Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

Büchel

Spanninger

Corman

2020

Sci Rep

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Transport networks are becoming increasingly large and interconnected. This interconnectivity is a key enabler of accessibility; on the other hand, it results in vulnerability, i.e. reduced performance, in case any specific part is subject to disruptions. We analyse how railway systems are vulnerable to delay, and how delays propagate in railway networks, studying real-life delay propagation phenomena on empirical data, determining real-life impact and delay propagation for the uncommon case of railway disruptions. We take a unique approach by looking at the same system, in two different operating conditions, to disentangle processes and dynamics that are normally present and co-occurring in railway operations. We exploit the unique chance to observe a systematic change in railway operations conditions, without a correspondent system change of infrastructure or timetable, coming from the occurrence of the large-scale disruption at Rastatt, Germany, in 2017. We define new statistical methods able to detect weak signals in the noisy dataset of recorded punctuality for passenger traffic in Switzerland, in the disrupted and undisrupted state, along a period of 1 year. We determine how delay propagation changed, and quantify the heterogeneous, large-scale cascading effects of the Rastatt disruption towards the Swiss network, hundreds of kilometers away. Operational measures of transport performance (i.e. punctuality and delays), while globally being very decreased, had a statistically relevant positive increase (though very geographically heterogeneous) on the Swiss passenger traffic during the disruption period. We identify two factors for this: (1) the reduced delay propagation at an international scale; and (2) to a minor extent, rerouted railway freight traffic; which show to combine linearly in the observed outcomes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

Büchel

Spanninger

Corman

2020

Sci Rep

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“…where () kl is traffic flow density at different locations l. Parameter l is a continuous variable with a range from the location of bus at the beginning of red light to the end of queuing vehicles, i.e., 1 1…”

Section: B Travel Time From the Detection Point To The Stop Linementioning

confidence: 99%

“…Meanwhile, because of the randomness of traffic flow and the influence of signal control at intersection, the travel time of buses fluctuates randomly, which reduces the stability of public transport. In order to further improve the level of bus service, based on these real-time information, a series of operational strategies is proposed for increasing the accuracy of timetable operation and the equilibrium of bus headway [1]- [3]. Then passengers can perceive the arrival time of buses well and the stability of passenger waiting time can be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

et al. 2020

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The arrival time of bus at the stop is critical data in design of bus operational strategies. Especially for real-time control strategies (i.e., signal priority system), bus arrival time is usually predicted to assess the next bus operation condition in the future (i.e., bus bunching and reliability of transit service) and then can be used as a decision basis of current control actions. The signalized intersection and surrounding traffic are the key factors in bus travel time prediction, but most previous approaches focus on the impact of signal control on bus delay only. This paper proposes a prediction model for arrival time at bus stop under the influence of both upstream signalized intersection and surrounding traffic flow. Considering the affected range of signalized intersection and the dynamic variation of bus speed, bus running processes are evaluated separately (including processes from a given detection point to stop line, through intersection, and from intersection to bus stop). In the proposed models, bus speed is deduced according to the change of traffic density at different locations to reflect the micro-impact of surrounding traffic flow on bus operation. The observed bus travel time is collected from actual investigations at two bus stops incorporating signalized intersection in Jinan City and compared with the predicted travel time in the proposed model. The results show that the proposed model has a low mean relative error. In addition, through the analysis of the maximum relative error, it also can be seen that vehicle queuing with random arrival of vehicles at stop line makes a gap between the prediction and the actual situation, which will be the focus of further research. INDEX TERMS Public transit, bus arrival time, signalized intersection, bus speed, traffic density.

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Assessing Infrastructure Resilience: Approaches and Considerations

Serdar,

Al‐Ghamdi

2023

Sustainable Cities in a Changing Climate

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Performance of transportation network under perturbations: Reliability, vulnerability, and resilience

Cited by 196 publications

References 84 publications

Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

Assessing Infrastructure Resilience: Approaches and Considerations

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