Merge pull request #24 from StanfordASL/djalota/update_in_press_entries

updated in press to published

_bibliography/ASL_Bib.bib

@@ -3401,7 +3401,6 @@ @article{JalotaPaccagnanEtAl2023
   title     = {On Online Traffic Routing: Deterministic Limits and Data-driven Enhancements},
   journal   = jrn_INFORMS_JOC,
   year      = {2023},
-  note      = {In Press},
   abstract  = {Over the past decade, GPS enabled traffic applications, such as Google Maps andWaze, 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 routing pattern. Spurred by the widespread impact of navigational 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 online solution of any deterministic algorithm and the optimal offline solution is unbounded, even in simplistic 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 a data-driven algorithm whose performance can be quantified and formally generalized to unseen future instances. Finally, we present numerical experiments based on two application cases for the San Francisco Bay Area and evaluate the performance of our approach. Our results show that the data-driven algorithm often outperforms commonly used greedy online routing algorithms, in particular, in scenarios where the user types are heterogeneous and the network is congested.},
   booktitle = jrn_INFORMS_JOC,
   keywords  = {press},
@@ -3413,7 +3412,7 @@ @article{JalotaPaccagnanEtAl2023
 @article{JalotaOstrovskyEtAl2023,
   author    = {Jalota, D. and Ostrovsky M. and Pavone, M.},
   title     = {Matching with Transfers under Distributional Constraints},
-  journal   = {Theoretical Economics},
+  journal   = jrn_Elsevier_GEB,
   year      = {2023},
   note      = {Submitted},
   keywords  = {sub},
@@ -3438,9 +3437,8 @@ @inproceedings{JalotaEtAl2022
   title     = {Online Learning for Traffic Routing under Unknown Preferences},
   booktitle = proc_AISTATS,
   year      = {2023},
-  note      = {In Press},
   abstract  = {In transportation networks, users typically choose routes in a decentralized and self-interested manner to minimize their individual travel costs, which, in practice, often results in inefficient overall outcomes for society. As a result, there has been a growing interest in designing road tolling schemes to cope with these efficiency losses and steer users toward a system-efficient traffic pattern. However, the efficacy of road tolling schemes often relies on having access to complete information on users' trip attributes, such as their origin-destination (O-D) travel information and their values of time, which may not be available in practice. Motivated by this practical consideration, we propose an online learning approach to set tolls in a traffic network to drive heterogeneous users with different values of time toward a system-efficient traffic pattern. In particular, we develop a simple yet effective algorithm that adjusts tolls at each time period solely based on the observed aggregate flows on the roads of the network without relying on any additional trip attributes of users, thereby preserving user privacy. In the setting where the O-D pairs and values of time of users are drawn i.i.d. at each period, we show that our approach obtains an expected regret and road capacity violation of $O(\sqrt{T})$, where $T$ is the number of periods over which tolls are updated. Our regret guarantee is relative to an offline oracle that has complete information on users' trip attributes. We further establish a $\Omega(\sqrt{T})$ lower bound on the regret of any algorithm, which establishes that our algorithm is optimal up to constants. Finally, we demonstrate the superior performance of our approach relative to several benchmarks on a real-world transportation network, thereby highlighting its practical applicability.},
-  keywords  = {press},
+  keywords  = {pub},
   owner     = {devanshjalota},
   timestamp = {2022-05-03},
   url       = {https://arxiv.org/abs/2203.17150}