Analyzing the Impact of Anticipatory Vehicle Routing on the Network Performance

Bilali, Aledia; Isaac, Gordon; Amini, Sasan; Motamedidehkordi, Nassim

doi:10.1016/j.trpro.2019.09.082

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…In parallel, the advent of big data analytics has led to a new paradigm in traffic routing, namely anticipatory route guidance [6,7]. In this context, the availability of prior traffic information enables the prediction of future demands and traffic conditions, which can then be incorporated in the routing recommendations.…”

Section: Introductionmentioning

confidence: 99%

Online Traffic Routing: Deterministic Limits and Data-driven Enhancements

Jalota,

Paccagnan,

Schiffer

et al. 2021

Preprint

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Over the past decade, GPS enabled traffic applications, such as Google Maps and Waze, have become ubiquitous and have had a significant influence on billions of daily commuters' travel patterns. A consequence of the online route suggestions of such applications, e.g., via greedy routing, has often been an increase in traffic congestion since the induced travel patterns may be far from the system optimum. Spurred by the widespread impact of traffic applications on travel patterns, this work studies online traffic routing in the context of capacity-constrained parallel road networks and analyzes this problem from two perspectives. First, we perform a worst-case analysis to identify the limits of deterministic online routing and show that the ratio between the total travel cost of the online solution of any deterministic algorithm and that of the optimal offline solution is unbounded, even in simple settings. This result motivates us to move beyond worst-case analysis. Here, we consider algorithms that exploit knowledge of past problem instances and show how to design data-driven algorithms whose performance can be quantified and formally generalized to unseen future instances. We present numerical experiments based on an application case for the San Francisco Bay Area to evaluate the performance of our approach. Our results show that the data-driven algorithms we develop outperform commonly used greedy online-routing algorithms.

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Section: Introductionmentioning

confidence: 99%

Online Traffic Routing: Deterministic Limits and Data-driven Enhancements

Jalota,

Paccagnan,

Schiffer

et al. 2021

Preprint

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“…With reference to proactive routing in general, a limited number of studies in the literature, especially recent ones, has been noticed. For the few proactive routing studies considering only travel time as the routing objective (Bottom, 2000;Ben-Akiva et al, 2001;Pan et al, 2013), the associated limitations were related to the scale of the case study (Kaufman et al, 1991), level of temporal and spatial resolution (Bilali et al, 2019), use of centralized solutions that suffer from scaling issues, and the use of reflective prediction models (Kaufman et al, 1991;Bottom, 2000;Ben-Akiva et al, 2001;Pan et al, 2013). The found proactive routing studies did not employ sophisticated predictive models, which is a major limitation.…”

Section: Introductionmentioning

confidence: 99%

“…The found proactive routing studies did not employ sophisticated predictive models, which is a major limitation. The forecasted travel time data points were provided by running the traffic simulation in advance (Ben-Akiva et al, 2001;Kim et al, 2016;Bilali et al, 2019) or based on regression models between speed and other traffic variables, such as density (Pan et al, 2013). In most of the studies, the travel time of time step t + i, obtained from the traffic simulation or historical data, was used in time step t for the proactive routing application, where i is the considered prediction interval (Ben-Akiva et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

Alfaseeh¹,

Farooq²

2020

Front. Future Transp.

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This study exploited the advancements in information and communication technology (ICT), connected and automated vehicles (CAVs), and sensing to develop proactive multi-objective eco-routing strategies for travel time and Greenhouse Gas (GHG) emissions reduction on urban road networks. For a robust application, several GHG costing approaches were examined. The predictive models for link level traffic and emission states were developed using the long short-term memory (LSTM) deep network with exogenous predictors. It was found that proactive routing strategies outperformed the reactive strategies regardless of the routing objective. Whether reactive or proactive, the multi-objective routing, with travel time and GHG minimization, outperformed the single objective routing strategies. Using a proactive multi-objective (travel time and GHG) routing strategy, we observed a reduction in average travel time (17%), average vehicle kilometer traveled (22%), total GHG (18%), and total nitrogen oxide (20%) when compared with the reactive single-objective (travel time).

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Online Routing Over Parallel Networks: Deterministic Limits and Data-driven Enhancements

Jalota

Paccagnan

Schiffer

et al. 2023

INFORMS Journal on Computing

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Over the past decade, GPS-enabled traffic applications such as Google Maps and Waze have become ubiquitous and have had a significant influence on billions of daily commuters’ travel patterns. A consequence of the online route suggestions of such applications, for example, via greedy routing, has often been an increase in traffic congestion since the induced travel patterns may be far from the system optimum. Spurred by the widespread impact of traffic applications on travel patterns, this work studies online traffic routing in the context of capacity-constrained parallel road networks and analyzes this problem from two perspectives. First, we perform a worst-case analysis to identify the limits of deterministic online routing. Although we find that deterministic online algorithms achieve finite, problem/instance-dependent competitive ratios in special cases, we show that for a general setting the competitive ratio is unbounded. This result motivates us to move beyond worst-case analysis. Here, we consider algorithms that exploit knowledge of past problem instances and show how to design data-driven algorithms whose performance can be quantified and formally generalized to unseen future instances. We then present numerical experiments based on an application case for the San Francisco Bay Area to evaluate the performance of the proposed data-driven algorithms compared with the greedy algorithm and two look-ahead heuristics with access to additional information on the values of time and arrival time parameters of users. Our results show that the developed data-driven algorithms outperform commonly used greedy online-routing algorithms. Furthermore, our work sheds light on the interplay between data availability and achievable solution quality. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms–Discrete. Funding: This work was supported by National Science Foundation (NSF) Award 1830554 and by the German Research Foundation (DFG) under [Grant 449261765]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2023.1275 .

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Analyzing the Impact of Anticipatory Vehicle Routing on the Network Performance

Cited by 3 publications

References 5 publications

Online Traffic Routing: Deterministic Limits and Data-driven Enhancements

Online Traffic Routing: Deterministic Limits and Data-driven Enhancements

Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

Online Routing Over Parallel Networks: Deterministic Limits and Data-driven Enhancements

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