Understanding traffic capacity of urban networks

Loder, Allister; Ambühl, Lukas; Menéndez, Mónica; Axhausen, Kay W.

doi:10.1038/s41598-019-51539-5

Cited by 144 publications

(85 citation statements)

References 37 publications

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“…omplex engineered systems are known to exhibit unintended states in their collective dynamics that often disrupt their function [1][2][3][4][5] . In complex mobility systems, examples include the emergence of congestion 6,7 , anomalous random walks in human travel patterns 8 , and cascading failures of mobility networks [9][10][11] . As urban mobility becomes more and more self-organized and digitized, mobility services increasingly employ dynamic pricing [12][13][14][15][16] , in general serving two main purposes ( Fig.…”

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confidence: 99%

Anomalous supply shortages from dynamic pricing in on-demand mobility

et al. 2020

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Dynamic pricing schemes are increasingly employed across industries to maintain a self-organized balance of demand and supply. However, throughout complex dynamical systems, unintended collective states exist that may compromise their function. Here we reveal how dynamic pricing may induce demand-supply imbalances instead of preventing them. Combining game theory and time series analysis of dynamic pricing data from on-demand ride-hailing services, we explain this apparent contradiction. We derive a phase diagram demonstrating how and under which conditions dynamic pricing incentivizes collective action of ride-hailing drivers to induce anomalous supply shortages. We identify characteristic patterns in the price dynamics reflecting these supply anomalies by disentangling different timescales in price time series of ride-hailing services at 137 locations across the globe. Our results provide systemic insights for the regulation of dynamic pricing, in particular in publicly accessible mobility systems, by unraveling under which conditions dynamic pricing schemes promote anomalous supply shortages.

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confidence: 99%

Anomalous supply shortages from dynamic pricing in on-demand mobility

et al. 2020

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“…Please note that one advantage of the NFD-based approach is that no DTA is generally necessary. The NFD can be estimated based on loop detector or floating car data [33], [34]. In the absence of data, analytical estimation methods can be applied to derive the NFD [35]- [38].…”

Section: Network Fundamental Diagrammentioning

confidence: 99%

Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

Dandl¹,

Tilg²,

Rostami-Shahrbabaki³

et al. 2020

Preprint

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The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

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“…Geroliminis and Daganzo (2008) confirmed the existence of the MFD by linking the vehicle accumulation to the average flow in a network with data from San Francisco and Yokohama. Recently, strong evidence for the unimodal MFD was reported for an extensive list of cities (Loder et al, 2019a). Moreover, first studies reported of the empirical evidence for the multi-modal MFD.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Design of Urban Bi-modal Transport Systems

Tilg¹,

Abedin²,

Amini³

et al. 2020

Front. Future Transp.

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The three-dimensional passenger macroscopic fundamental diagram (pMFD) describes the relation of the network accumulation of public transport and private vehicles, and the passenger production. It allows for modeling the multi-modal traffic dynamics in urban networks and deriving innovative performance indicators. This paper integrates this concept into a multi-modal transport system design framework formulated as a simulation-based optimization problem. In doing so, we consider the competition for limited road space and the operational characteristics, such as congestion occurrences, at the strategic design level. We evaluate the proposed framework in a case study for the Sioux Falls network. Thereby, we deliver a proof of concept, and show that the proposed methodology indeed designs a transport system which benefits the overall system's performance. This paper further advances the integration of sequential model-based optimization techniques, macroscopic traffic flow concepts, and traffic simulation to design multi-modal transport systems. This supports transport planners and local authorities in composing efficient and robust transport networks.

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Understanding traffic capacity of urban networks

Cited by 144 publications

References 37 publications

Anomalous supply shortages from dynamic pricing in on-demand mobility

Anomalous supply shortages from dynamic pricing in on-demand mobility

Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

Simulation-Based Design of Urban Bi-modal Transport Systems

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