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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Mo, B"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.03276">arXiv:2401.03276</a> <span> [<a href="https://arxiv.org/pdf/2401.03276">pdf</a>, <a href="https://arxiv.org/ps/2401.03276">ps</a>, <a href="https://arxiv.org/format/2401.03276">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Robust Discrete Choice Model for Travel Behavior Prediction With Data Uncertainties </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/math?searchtype=author&query=Mo%2C+B">Baichuan Mo</a>, <a href="/search/math?searchtype=author&query=Zheng%2C+Y">Yunhan Zheng</a>, <a href="/search/math?searchtype=author&query=Guo%2C+X">Xiaotong Guo</a>, <a href="/search/math?searchtype=author&query=Ma%2C+R">Ruoyun Ma</a>, <a href="/search/math?searchtype=author&query=Zhao%2C+J">Jinhua Zhao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.03276v1-abstract-short" style="display: inline;"> Discrete choice models (DCMs) are the canonical methods for travel behavior modeling and prediction. However, in many scenarios, the collected data for DCMs are subject to measurement errors. Previous studies on measurement errors mostly focus on "better estimating model parameters" with training data. In this study, we focus on "better predicting new samples' behavior" when there are measurement… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03276v1-abstract-full').style.display = 'inline'; document.getElementById('2401.03276v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.03276v1-abstract-full" style="display: none;"> Discrete choice models (DCMs) are the canonical methods for travel behavior modeling and prediction. However, in many scenarios, the collected data for DCMs are subject to measurement errors. Previous studies on measurement errors mostly focus on "better estimating model parameters" with training data. In this study, we focus on "better predicting new samples' behavior" when there are measurement errors in testing data. To this end, we propose a robust discrete choice model framework that is able to account for data uncertainties in both features and labels. The model is based on robust optimization theory that minimizes the worst-case loss over a set of uncertainty data scenarios. Specifically, for feature uncertainties, we assume that the $\ell_p$-norm of the measurement errors in features is smaller than a pre-established threshold. We model label uncertainties by limiting the number of mislabeled choices to at most $螕$. Based on these assumptions, we derive a tractable robust counterpart for robust-feature and robust-label DCM models. The derived robust-feature binary logit (BNL) and the robust-label multinomial logit (MNL) models are exact. However, the formulation for the robust-feature MNL model is an approximation of the exact robust optimization problem. The proposed models are validated in a binary choice data set and a multinomial choice data set, respectively. Results show that the robust models (both features and labels) can outperform the conventional BNL and MNL models in prediction accuracy and log-likelihood. We show that the robustness works like "regularization" and thus has better generalizability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03276v1-abstract-full').style.display = 'none'; document.getElementById('2401.03276v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.02102">arXiv:2302.02102</a> <span> [<a href="https://arxiv.org/pdf/2302.02102">pdf</a>, <a href="https://arxiv.org/format/2302.02102">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Amazon Last-Mile Delivery Trajectory Prediction Using Hierarchical TSP with Customized Cost Matrix </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/math?searchtype=author&query=Guo%2C+X">Xiaotong Guo</a>, <a href="/search/math?searchtype=author&query=Mo%2C+B">Baichuan Mo</a>, <a href="/search/math?searchtype=author&query=Wang%2C+Q">Qingyi Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.02102v1-abstract-short" style="display: inline;"> In response to the Amazon Last-Mile Routing Challenge, Team Permission Denied proposes a hierarchical Travelling Salesman Problem (TSP) optimization with a customized cost matrix. The higher level TSP solves for the zone sequence while the lower level TSP solves the intra-zonal stop sequence. The cost matrix is modified to account for routing patterns beyond the shortest travel time. Lastly, some… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02102v1-abstract-full').style.display = 'inline'; document.getElementById('2302.02102v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.02102v1-abstract-full" style="display: none;"> In response to the Amazon Last-Mile Routing Challenge, Team Permission Denied proposes a hierarchical Travelling Salesman Problem (TSP) optimization with a customized cost matrix. The higher level TSP solves for the zone sequence while the lower level TSP solves the intra-zonal stop sequence. The cost matrix is modified to account for routing patterns beyond the shortest travel time. Lastly, some post-processing is done to edit the sequence to match commonly observed routing patterns, such as when travel times are similar, drivers usually start with stops with more packages than those with fewer packages. The model is tested on 1223 routes that are randomly selected out of the training set and the score is 0.0381. On the 13 routes in the given model apply set, the score was 0.0375. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02102v1-abstract-full').style.display = 'none'; document.getElementById('2302.02102v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.00918">arXiv:2301.00918</a> <span> [<a href="https://arxiv.org/pdf/2301.00918">pdf</a>, <a href="https://arxiv.org/format/2301.00918">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Evaluation of Public Transit Systems under Short Random Service Suspensions: A Bulk-Service Queuing Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/math?searchtype=author&query=Mo%2C+B">Baichuan Mo</a>, <a href="/search/math?searchtype=author&query=Jin%2C+L">Li Jin</a>, <a href="/search/math?searchtype=author&query=Koutsopoulos%2C+H+N">Haris N. Koutsopoulos</a>, <a href="/search/math?searchtype=author&query=Shen%2C+Z+M">Zuo-Jun Max Shen</a>, <a href="/search/math?searchtype=author&query=Zhao%2C+J">Jinhua Zhao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.00918v1-abstract-short" style="display: inline;"> This paper proposes a stochastic framework to evaluate the performance of public transit systems under short random service suspensions. We aim to derive closed-form formulations of the mean and variance of the queue length and waiting time. A bulk-service queue model is adopted to formulate the queuing behavior in the system. The random service suspension is modeled as a two-state (disruption and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.00918v1-abstract-full').style.display = 'inline'; document.getElementById('2301.00918v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.00918v1-abstract-full" style="display: none;"> This paper proposes a stochastic framework to evaluate the performance of public transit systems under short random service suspensions. We aim to derive closed-form formulations of the mean and variance of the queue length and waiting time. A bulk-service queue model is adopted to formulate the queuing behavior in the system. The random service suspension is modeled as a two-state (disruption and normal) Markov process. We prove that headway is distributed as the difference between two compound Poisson exponential random variables. The distribution is used to specify the mean and variance of queue length and waiting time at each station with analytical formulations. The closed-form stability condition of the system is also derived, implying that the system is more likely to be unstable with high incident rates and long incident duration. The proposed model is implemented on a bus network. Results show that higher incident rates and higher average incident duration will increase both the mean and variance of queue length and waiting time, which are consistent with the theoretical analysis. Crowding stations are more vulnerable to random service suspensions. The theoretical results are validated with a simulation model, showing consistency between the two outcomes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.00918v1-abstract-full').style.display = 'none'; document.getElementById('2301.00918v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.00916">arXiv:2301.00916</a> <span> [<a href="https://arxiv.org/pdf/2301.00916">pdf</a>, <a href="https://arxiv.org/format/2301.00916">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Individual Path Recommendation Under Public Transit Service Disruptions Considering Behavior Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/math?searchtype=author&query=Mo%2C+B">Baichuan Mo</a>, <a href="/search/math?searchtype=author&query=Koutsopoulos%2C+H+N">Haris N. Koutsopoulos</a>, <a href="/search/math?searchtype=author&query=Shen%2C+Z+M">Zuo-Jun Max Shen</a>, <a href="/search/math?searchtype=author&query=Zhao%2C+J">Jinhua Zhao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.00916v1-abstract-short" style="display: inline;"> This study proposes a mixed-integer programming formulation to model the individual-based path (IPR) recommendation problem during public transit service disruptions with the objective of minimizing system travel time and respecting passengers' path choice preferences. Passengers' behavior uncertainty in path choices given recommendations is also considered. We model the behavior uncertainty based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.00916v1-abstract-full').style.display = 'inline'; document.getElementById('2301.00916v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.00916v1-abstract-full" style="display: none;"> This study proposes a mixed-integer programming formulation to model the individual-based path (IPR) recommendation problem during public transit service disruptions with the objective of minimizing system travel time and respecting passengers' path choice preferences. Passengers' behavior uncertainty in path choices given recommendations is also considered. We model the behavior uncertainty based on the passenger's prior preferences and posterior path choice probability distribution with two new concepts: epsilon-feasibility and Gamma-concentration, which control the mean and variance of path flows in the optimization problem. We show that these two concepts can be seen as a way of approximating the recourse function (expected system travel time) in a two-stage stochastic optimization. It is proved that these two concepts help to bound the difference between the approximated recourse function and the exact one. Additional theoretical analysis shows that epsilon-feasibility and Gamma-concentration can be seen as an approximation of expectation and chance constraints in a typical stochastic optimization formulation, respectively. The proposed IPR problem with behavior uncertainty is solved efficiently with Benders decomposition. The model is implemented in the Chicago Transit Authority (CTA) system with a real-world urban rail disruption as the case study. Results show that the proposed IPR model significantly reduces the average travel times compared to the status quo and outperforms the capacity-based benchmark path recommendation strategy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.00916v1-abstract-full').style.display = 'none'; document.getElementById('2301.00916v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.12666">arXiv:2204.12666</a> <span> [<a href="https://arxiv.org/pdf/2204.12666">pdf</a>, <a href="https://arxiv.org/format/2204.12666">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Transit Frequency Setting Problem with Demand Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/math?searchtype=author&query=Guo%2C+X">Xiaotong Guo</a>, <a href="/search/math?searchtype=author&query=Mo%2C+B">Baichuan Mo</a>, <a href="/search/math?searchtype=author&query=Koutsopoulos%2C+H+N">Haris N. Koutsopoulos</a>, <a href="/search/math?searchtype=author&query=Wang%2C+S">Shenhao Wang</a>, <a href="/search/math?searchtype=author&query=Zhao%2C+J">Jinhua Zhao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.12666v2-abstract-short" style="display: inline;"> Public transit systems are the backbone of urban mobility systems in the era of urbanization. The design of transit schedules is important for the efficient and sustainable operation of public transit. However, previous studies usually assume fixed demand patterns and ignore uncertainties in demand, which may generate transit schedules that are vulnerable to demand variations. To address demand un… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.12666v2-abstract-full').style.display = 'inline'; document.getElementById('2204.12666v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.12666v2-abstract-full" style="display: none;"> Public transit systems are the backbone of urban mobility systems in the era of urbanization. The design of transit schedules is important for the efficient and sustainable operation of public transit. However, previous studies usually assume fixed demand patterns and ignore uncertainties in demand, which may generate transit schedules that are vulnerable to demand variations. To address demand uncertainty issues inherent in public transit systems, this paper adopts both stochastic programming (SP) and robust optimization (RO) techniques to generate robust transit schedules against demand uncertainty. A nominal (non-robust) optimization model for the transit frequency setting problem (TFSP) under a single transit line setting is first proposed. The model is then extended to SP-based and RO-based formulations to incorporate demand uncertainty. The large-scale origin-destination (OD) matrices for real-world transit problems make the optimization problems hard to solve. To efficiently generate robust transit schedules, a Transit Downsizing (TD) approach is proposed to reduce the dimensionality of the problem. We prove that the optimal objective function of the problem after TD is close to that of the original problem (i.e., the difference is bounded from above). The proposed models are tested with real-world transit lines and data from the Chicago Transit Authority (CTA). Compared to the current transit schedule implemented by CTA, the nominal TFSP model without considering demand uncertainty reduces passengers' wait times while increasing in-vehicle travel times. After incorporating demand uncertainty, both stochastic and robust TFSP models reduce passengers' wait times and in-vehicle travel times simultaneously. The robust TFSP model produces transit schedules with better in-vehicle travel times and worse wait times for passengers compared to the stochastic TFSP model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.12666v2-abstract-full').style.display = 'none'; document.getElementById('2204.12666v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">35 pages, 22 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.01437">arXiv:2201.01437</a> <span> [<a href="https://arxiv.org/pdf/2201.01437">pdf</a>, <a href="https://arxiv.org/format/2201.01437">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/math?searchtype=author&query=Mo%2C+B">Baichuan Mo</a>, <a href="/search/math?searchtype=author&query=Koutsopoulos%2C+H+N">Haris N. Koutsopoulos</a>, <a href="/search/math?searchtype=author&query=Shen%2C+M+Z">Max Zuo-Jun Shen</a>, <a href="/search/math?searchtype=author&query=Zhao%2C+J">Jinhua Zhao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.01437v1-abstract-short" style="display: inline;"> When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate the congestion during public transit disruptions. Passengers with different origin-destination and departure times are recommended with different paths such that the system travel time is minimized. We mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.01437v1-abstract-full').style.display = 'inline'; document.getElementById('2201.01437v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.01437v1-abstract-full" style="display: none;"> When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate the congestion during public transit disruptions. Passengers with different origin-destination and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation as an optimal flow problem with uncertain demand information. To tackle the non-analytical formulation of travel times due to left behind, we propose a simulation-based first-order approximation to transform the original problem into linear programming. Uncertainties in demand are modeled with robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (-20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce the system travel time. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.01437v1-abstract-full').style.display = 'none'; document.getElementById('2201.01437v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1602.03230">arXiv:1602.03230</a> <span> [<a href="https://arxiv.org/pdf/1602.03230">pdf</a>, <a href="https://arxiv.org/ps/1602.03230">ps</a>, <a href="https://arxiv.org/format/1602.03230">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> </div> </div> <p class="title is-5 mathjax"> Sharp bounds for ordinary and signless Laplacian spectral radii of uniform hypergraphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/math?searchtype=author&query=Lin%2C+H">Hongying Lin</a>, <a href="/search/math?searchtype=author&query=Mo%2C+B">Biao Mo</a>, <a href="/search/math?searchtype=author&query=Zhou%2C+B">Bo Zhou</a>, <a href="/search/math?searchtype=author&query=Weng%2C+W">Weiming Weng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1602.03230v2-abstract-short" style="display: inline;"> We give sharp upper bounds for the ordinary spectral radius and signless Laplacian spectral radius of a uniform hypergraph in terms of the average $2$-degrees or degrees of vertices, respectively, and we also give a lower bound for the ordinary spectral radius. We also compare these bounds with known ones. </span> <span class="abstract-full has-text-grey-dark mathjax" id="1602.03230v2-abstract-full" style="display: none;"> We give sharp upper bounds for the ordinary spectral radius and signless Laplacian spectral radius of a uniform hypergraph in terms of the average $2$-degrees or degrees of vertices, respectively, and we also give a lower bound for the ordinary spectral radius. We also compare these bounds with known ones. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.03230v2-abstract-full').style.display = 'none'; document.getElementById('1602.03230v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 May, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 February, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 05C65 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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