Critical street links for demand responsive feeder transit services

Chandra, Shailesh; Quadrifoglio, Luca

doi:10.1016/j.cie.2013.04.004

Cited by 18 publications

(8 citation statements)

References 21 publications

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“…One aspect that has received surprisingly little attention in the literature is the connection between urban structure and vulnerability. Some studies have looked at where the most critical links in an urban street network are located (Chandra and Quadrifoglio, 2013;Ortigosa and Menendez, 2014) and possibilities to mitigate vulnerability by land-use strategies (Zhao et al, 2014). We also discussed how a bridge that replaces unreliable ferry lines can increase the economic integration in the region and in the end induce a new kind of vulnerability.…”

Section: Discussion Of the Approachesmentioning

confidence: 99%

“…Cats and Jenelius (2015) study the possibility of reducing vulnerability by increasing the capacity on lines that can serve as alternatives when critical links are disrupted, and propose a methodology for identifying the lines where capacity increases are the most effective. Chandra and Quadrifoglio (2013) develop a methodology for identifying critical links for demand responsive feeder services in a stylised grid street network. Assuming uniform demand between the node pairs in the network, they derive an approximate analytical expression for the increase in total demand-weighted travel distance if one or more links in the street network are removed.…”

Section: Rail and Public Transport Networkmentioning

confidence: 99%

See 1 more Smart Citation

Vulnerability and resilience of transport systems – A discussion of recent research

Mattsson

Jenelius

2015

Transportation Research Part A: Policy and Practice

463

333

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Section: Discussion Of the Approachesmentioning

confidence: 99%

Section: Rail and Public Transport Networkmentioning

confidence: 99%

Vulnerability and resilience of transport systems – A discussion of recent research

Mattsson

Jenelius

2015

Transportation Research Part A: Policy and Practice

463

333

View full text Add to dashboard Cite

“…The critical links are then identified by evaluating all possible link closures. (7) There is no doubt that this process is computationally expensive, even for simple and symmetrical grid street networks (17) and therefore almost impossible to apply to large networks. The LRSRM offers improvements with regard to computation time, for example, which is a significant barrier to applying models to real-world problems.…”

Section: Literature Reviewmentioning

confidence: 99%

The Multiscale Importance of Road Segments in a Network Disruption Scenario: A Risk‐Based Approach

2014

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This article addresses the problem of the multiscale importance of road networks, with the aim of helping to establish a more resilient network in the event of a road disruption scenario. A new model for identifying the most important roads is described and applied on a local and regional scale. The work presented here represents a step forward, since it focuses on the interaction between identifying the most important roads in a network that connect people and health services, the specificity of the natural hazards that threaten the normal functioning of the network, and an assessment of the consequences of three real-world interruptions from a multiscale perspective. The case studies concern three different past events: road interruptions due to a flood, a forest fire, and a mass movement. On the basis of the results obtained, it is possible to establish the roads for which risk management should be a priority. The multiscale perspective shows that in a road interruption the regional system may have the capacity to reorganize itself, although the interruption may have consequences for local dynamics. Coordination between local and regional scales is therefore important. The model proposed here allows for the scaling of emergency response facilities and human and physical resources. It represents an innovative approach to defining priorities, not only in the prevention phase but also in terms of the response to natural disasters, such as awareness of the consequences of road disruption for the rescue services sent out to local communities.

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“…Constraint (4) ensures that at most v vehicles participate in operation services. Constraint (5) indicates that except the first demand point of each vehicle, for every other demand point being serviced, the number of entering vehicles should be equal to the ones that are driven out. Constraint (6) is to avoid loops in the driving path.…”

Section: Model Representationmentioning

confidence: 99%

“…Currently, there are relatively few research studies on flexible bus service systems especially on multiple flexible stations corresponding to one target station. By constructing a model [3,4], the relationships between key parameters such as slack time [5], departure interval, and several bus routes are analyzed. For example, Lu et al [6] proposed a three-stage model to find the nearest vehicle to serve passenger demands when many buses are operating in the target service area.…”

Section: Introductionmentioning

confidence: 99%

Flexible Bus Route Optimization for Multitarget Stations

Sun

Huang

Chen

et al. 2020

Mathematical Problems in Engineering

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is paper proposes a flexible bus route optimization model for efficient public city transportation systems based on multitarget stations. e model considers passenger demands, vehicle capacities, and transportation network and aims to solve the optimal route, minimizing the vehicles' running time and the passengers' travel time. A heuristic algorithm based on a gravity model is introduced to solve this NP-hard optimization problem. Simulation studies verify the effectiveness and practicality of the proposed model and algorithm. e results show that the total number of vehicles needed to complete the service is 17-21, the average travel time of each vehicle is 24.59 minutes, the solving time of 100 sets of data is within 25 seconds, and the average calculation time is 12.04 seconds. It can be seen that under the premise of real-time adjustment of connection planning time, the optimization model can satisfy the passenger's dynamic demand to a greater extent, and effectively reduce the planning path error, shorten the distance and travel time of passengers, and the result is better than that of the flexible bus scheduling model which ignores the change of connection travel time.

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Critical street links for demand responsive feeder transit services

Cited by 18 publications

References 21 publications

Vulnerability and resilience of transport systems – A discussion of recent research

Vulnerability and resilience of transport systems – A discussion of recent research

The Multiscale Importance of Road Segments in a Network Disruption Scenario: A Risk‐Based Approach

Flexible Bus Route Optimization for Multitarget Stations

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