ABSTRACTIn many ‘smart city’ applications, congestion arises in part due to the nature of signals received by individuals from a central authority. In the model of , each agent uses one out of multiple resources at each time instant. The per-use cost of a resource depends on the number of concurrent users. A central authority has up-to-date knowledge of the congestion across all resources and uses randomisation to provide a scalar or an interval for each resource at each time. In this paper, the interval to broadcast per resource is obtained by taking the minima and maxima of costs observed within a time window of length r, rather than by randomisation. We show that the resulting distribution of agents across resources also converges in distribution, under plausible assumptions about the evolution of the population over time.
In the transportation literature, Daganzo and Sheffi @cite_49 have introduced the concept of stochastic user equilibrium, where users have considerable amounts of information, and perhaps surprising analytical powers. Subsequently, a number of variants have been proposed, e.g., @cite_31 consider robust variants and @cite_54 consider the stochastic user equilibrium with distributional uncertainty over the travel times. @cite_21 consider a stratified and risk-averse population, but only as much as a link failure is concerned. There are a number of other notions of stability, perhaps closer to our notion, inasmuch they capture the repeated nature of the problem. For example, @cite_50 introduces the notion of equilibrium as the limit of the congestion distribution if it exists. @cite_14 considers a number of notions of noisy signals and studies greedy policies and equilibria. Our approach is different from those that assign actions to agents, instead of presenting them with information and letting them make the decisions.
@cite_14 @cite_54 @cite_21 @cite_50 @cite_49 @cite_31
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The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15 . Moreover, improvements of up to 41 can be achieved for some test instances. In the traffic assignment problem, first proposed by Wardrop in 1952, commuters select the shortest available path to travel from their origins to their destinations. We study a generalization of this problem in which competitors, who may control a nonnegligible fraction of the total flow, ship goods across a network. This type of games, usually referred to as atomic games, readily applies to situations in which the competing freight companies have market power. Other applications include intelligent transportation systems, competition among telecommunication network service providers, and scheduling with flexible machines. Our goal is to determine to what extent these systems can benefit from some form of coordination or regulation. We measure the quality of the outcome of the game without centralized control by computing the worst-case inefficiency of Nash equilibria. The main conclusion is that although self-interested competitors will not achieve a fully efficient solution from the system's point of view, the loss is not too severe. We show how to compute several bounds for the worst-case inefficiency that depend on the characteristics of cost functions and on the market structure in the game. In addition, building upon the work of Catoni and Pallotino, we show examples in which market aggregation (or collusion) adversely impacts the aggregated competitors, even though their market power increases. For example, Nash equilibria of atomic network games may be less efficient than the corresponding Wardrop equilibria. When competitors are completely symmetric, we provide a characterization of the Nash equilibrium using a potential function, and prove that this counterintuitive phenomenon does not arise. Finally, we study a pricing mechanism that elicits more coordination from the players by reducing the worst-case inefficiency of Nash equilibria. Traditionally, traffic assignment models, both for within-day static and dynamic demand, have been formulated following an equilibrium approach in which a state ensuring internal consistency between demand (flows) and costs is sought. However, equilibrium analysis is significant under some assumptions on its “representativeness” (coincidence or closeness with the actual attractor of the system) and analytical properties, such as existence, uniqueness, and stability. Moreover, transients due to modifications of demand and or supply cannot be simulated through equilibrium models, nor can a statistical description of the state of the system, i.e. means, modes, moments and, more generally, frequency distributions of flows over time be obtained. In this paper, interperiodic (day-to-day) dynamic modeling of transportation networks is addressed following two different approaches, namely deterministic and stochastic processes. In both cases several theoretical results are shown by making use of a formal framework covering most models discussed in the literature as well as some possible extensions. Most of the results reported can be extended to cover within-day dynamic models but these models are not explicitly dealt with. Within the framework of deterministic processes the relevance of day-to-day dynamic models for demand supply interaction in comparison with the traditional user equilibrium approach is discussed, and conditions for coincidence of fixed-point attractors and equilibrium states are stated. Conditions for existence and uniqueness of fixed-point attractors are proposed, generalizing and extending those presented in the literature for user equilibrium. Conditions for stability of both fixed-points and equilibrium states were formulated by making use of results from non-linear dynamic system theory. Moreover, it is possible to devise a new family of “dynamic” algorithms which simulate the system convergence to a fixed-point in order to obtain an equivalent equilibrium state, as opposed to conventional “optimisation” algorithms. In this case the fixed-point stability analysis can be viewed as a convergence analysis for the algorithms specified this way. Conditions for stochastic process regularity are proposed ensuring, among other things, existence and uniqueness of a stationary probability distribution of system states. These conditions generalize and extend results presented in the literature to a wider class of possible dynamic models. Relationships between a deterministic process, together with corresponding fixed-points or equilibrium states, and stochastic probability distribution are also briefly addressed. Finally, some numerical examples confirming theoretical results are reported for a small test network. This paper contains a quantitative evaluation of probabilistic traffic assignment models and proposes an alternate formulation. First, the concept of stochastic-user-equilibration (S-U-E) is formalized as an extension of Wardrop's user-equilibration criterion. Then, the stochastic-network-loading (S-N-L) problem (a special case of S-U-E for networks with constant link costs) is analyzed in detail and an expression for the probability of route choice which is based on two general postulates of user behavior is derived. The paper also discusses the weaknesses of existing S-N-L techniques with special attention paid to Dial's multipath method and compares them to the suggested approach. The proposed model seems reasonable and does not exhibit the inherent weaknesses of the logit model when applied to sets of routes which overlap heavily. The discussion is supported by several numerical examples on small contrived networks. The paper concludes with the discussion of two techniques that can be used to approximate the link flows resulting from the proposed model in large networks. We study the question of whether a large population of agents in a traffic network is able to converge to an equilibrium quickly. To that end, we consider a round-based variant of the Wardrop model. Every agent is allowed to reroute its traffic once in a while with the aim of finding a path with minimal latency. As a first result we find that using a replication policy which allows agents to imitate others gives rise to a bicriterial approximate equilibrium very quickly. In particular, the time bound depends logarithmically on the ratio between minimum and maximum latency but is otherwise independent of the network size. In the single-commodity case, this bicriteria approximate equilibrium has an intuitive interpretation as a state in which almost all agents are almost happy. This kind of approximate equilibrium, however, is transient. In order to reach a global approximation, we need to add an exploration component which enables the agents to explore the strategy space independently of the other agents. Although it can be shown that, when used exclusively, exploration policies imply an exponential lower bound, applying exploration carefully allows the population to approximate the global Wardrop equilibrium in polynomial time. Since the distributed and concurrent fashion of our policies bears the risk of oscillating behavior, we must take into account the steepness of the latency functions. We show that the relevant parameter is elasticity, a parameter closely related to the polynomial degree. This improves significantly over earlier results which depend on the absolute slope and therefore have a pseudopolynomial flavor. The Stochastic User Equilibrium (SUE) model predicts traffic equilibrium flow assuming that users choose their perceived maximum utility paths (or perceived shortest paths) while accounting for the effects of congestion that arise due to users sharing links. Inspired by recent work on distributionally robust optimization, specifically a Cross Moment (CMM) choice model, we develop a new SUE model that uses the mean and covariance information on path utilities but does not assume the particular form of the distribution. Robustness to distributional assumptions is obtained in this model by minimizing the worst-case expected cost over all distributions with fixed two moments. We show that under mild conditions, the CMM-SUE (Cross Moment-Stochastic User Equilibrium) exists and is unique. By combining a simple projected gradient ascent method to evaluate path choice probabilities with a gradient descent method to find flows, we show that the CMM-SUE is efficiently computable. CMM-SUE provides both modeling flexibility and computational advantages over approaches such as the well-known MNP-SUE (Multinomial Probit-Stochastic User Equilibrium) model that require distributional (normality) assumptions to model correlation effects from overlapping paths. In particular, it avoids the use of simulation methods employed in computations for the distribution-based MNP-SUE model. Preliminary computational results indicate that CMM-SUE provides a practical distributionally robust alternative to MNP-SUE. Most research and applications of network equilibrium models are based on the assumption that traffic volumes on roadways are virtually certain to be at or near their equilibrium values if the equilibrium volumes exist and are unique. However, it has long been known that this assumption can be violated in deterministic models. This paper presents an investigation of the stability of stochastic equilibrium in a two-link network. The stability of deterministic equilibrium also is discussed briefly. Equilibrium is defined to be stable if it is unique and the link volumes converge over time to their equilibrium values regardless of the initial conditions. Three models of route choice decision-making over time are formulated, and the stability of equilibrium is investigated for each. It is shown that even when equilibrium is unique, link volumes may converge to their equilibrium values, oscillate about equilibrium perpetually, or converge to values that may be considerably different from the equilibrium ones, depending on the details of the route choice decision-making process. Moreover, even when convergence of link volumes to equilibrium is assured, the convergence may be too slow to justify the standard assumption that these volumes are usually at or near their equilibrium values. When link volumes converge to non-equilibrium values, the levels at which the volumes stabilize typically depend on the initial link volumes or perceptions of travel costs. Conditions sufficient to assure convergence to equilibrium in two of the three models of route choice decision-making are presented, and these conditions are interpreted in terms of the route choice decision-making process. In 1991, D. J. Bertsimas and G. van Ryzin introduced and analyzed a model for stochastic and dynamic vehicle routing in which a single, uncapacitated vehicle traveling at a constant velocity in a Euclidean region must service demands whose time of arrival, location and on-site service are stochastic. The objective is to find a policy to service demands over an infinite horizon that minimizes the expected system time (wait plus service) of the demands. This paper extends our analysis in several directions. First, we analyze the problem of m identical vehicles with unlimited capacity and show that in heavy traffic the system time is reduced by a factor of 1 m2 over the single-server case. One of these policies improves by a factor of two on the best known system time for the single-server case. We then consider the case in which each vehicle can serve at most q customers before returning to a depot. We show that the stability condition in this case depends strongly on the geometry of the region. Several pol... We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. The notions of user equilibrium (UE) and system optimum (SO) often allude to the literature together with the well-known principle of marginal-cost pricing in traffic network analyses. This pricing principle states that the UE flow pattern on a network can be driven to an SO in the sense of total travel cost minimization by charging a toll on each link equal to the difference between marginal social cost and marginal private cost. In reality, users do not always behave in a UE manner, typically when there exist oligopoly Cournot-Nash (CN) firms. Users in a CN firm cooperate among themselves to minimize total cost of the firm and compete against others. In the presence of such UE-CN mixed equilibrium behaviors, we are interested in whether an SO flow pattern remains attainable by meaningful link tolls. In this paper we show that in a network with both UE and CN users, applying the traditional marginal-cost pricing for a system optimum requires that link tolls be differentiated across user classes. Because users differ from one another in an unobservable way, it is impossible to introduce discriminatory tolling on a network in a mixed behaviour equilibrium. We then seek alternative meaningful tolls by establishing the existence of nonnegative anonymous link tolls to decentralize the SO into a UE-CN mixed behavior equilibrium with resort to a rigorous mathematical programming approach. This paper considers the effect of stochasticity on the quality of convergence of genetic algorithms (GAs). In many problems, the variance of building-block fitness or so-called collateral noise is the major source of variance, and a population-sizing equation is derived to ensure that average signal-to-collateral-noise ratios are favorable to the discrimination of the best building blocks required to solve a problem of bounded deception. The sizing relation is modified to permit the inclusion of other sources of stochasticity, such as the noise of selection, the noise of genetic operators, and the explicit noise or nondeterminism of the objective function. In a test suite of five functions, the sizing relation proves to be a conservative predictor of average correct convergence, as long as all major sources of noise are considered in the sizing calculation. These results suggest how the sizing equation may be viewed as a coarse delineation of a boundary between what a physicist might call two distinct phases of GA behavior. At low population sizes the GA makes many errors of decision, and the quality of convergence is largely left to the vagaries of chance or the serial fixup of flawed results through mutation or other serial injection of diversity. At large population sizes, GAs can reliably discriminate between good and bad building blocks, and parallel processing and recombination of building blocks lead to quick solution of even difficult deceptive problems. Additionally, the paper outlines a number of extensions to this work, including the development of more refined models of the relation between generational average error and ultimate convergence quality, the development of online methods for sizing populations via the estimation of populations via the estimation of population-sizing parameters, and the investigation of populationsizing in the context of niching and other schemes designed for use in problems with high cardinality solution sets. The paper also discusses how these results may one day lead to rigorous proofs of convergence for recombinative G As operating on problems of bounded description. THE CAPABILITIES AND EFFICIENT OPERATION OF SEVERAL TYPES OF TRANSPORTATION SYSTEMS ARE STUDIED. FOR HIGHWAY TRAFFIC THE MAIN EMPHASIS IS ON CONGESTION AND ITS CONSEQUENCES. QUEUING MODELS ARE USED TO DETERMINE FLUCTUATIONS IN WAITING TIME FOR TWO CASES, THE FLOW OF CARS THROUGH AN INTERSECTION AND THE PASSING OF SLOWER CARS BY FASTER CARS ON A TWO-LANE HIGHWAY. THE RESULTS OF THESE STUDIES ARE INCORPORATED INTO A NON-LINEAR MODEL OF HIGHWAY TRAFFIC BEHAVIOR ON A ROAD NETWORK. THE CAPACITIES ARE EXPRESSED BY THE RELATIONSHIP BETWEEN TRAFFIC FLOWS ON ALL ROUTES AND THE COSTS ENCOUNTERED ON EACH AS A RESULT OF THESE FLOWS. HIGHWAY TRAFFIC DEMANDS ARE THE RESULT OF A GREAT MANY INDIVIDUAL DECISIONS ABOUT DESTINATION, ROUTES, AND PREFERRED SPEEDS. THE ANALOGY BETWEEN HIGHWAY TRAFFIC EQUILIBRIUM AND THE MARKET RESPONSE MODELS USED BY ECONOMISTS IS GIVEN. IF THE INDIVIDUALLY INCURRED COST OF TRANSPORTATION IS REGARDED AS THE "PRICE" IN THE TRANSPORTATION "MARKET," THE ECONOMIST'S DEMAND CURVE BECOMES APPLICABLE. FOR A SINGLE ROAD IT GIVES THE FLOW OF TRAFFIC DEMANDED FOR ANY GIVEN COST ENCOUNTERED ON THAT ROAD. GENERALLY, THE HIGHER THE COST, THE SMALLER THE FLOW. CONSIDERING A ROAD NETWORK THE DEMAND FUNCTION GIVES THE FLOWS ON EACH ROUTE IN RESPONSE TO A SET OF TRANSPORTATION COSTS ALONG THESE ROUTES. EQUILIBRIUM EXISTS IF THE FLOWS ON ALL ROADS RESULTING FROM THESE COSTS ARE THE SAME AS THE FLOWS THAT PRODUCE THESE SAME COSTS. THIS CONCEPT IS APPLIED TO A STUDY OF TRAFFIC EQUILIBRIUM AND USE OF THE ANALYSIS IN THE PREDICTION OF TRAFFIC FLOWS IS DISCUSSED. THE ANALYSIS OF DEMAND AND OF EQUILIBRIUM RECOGNIZES THAT FREEDOM OF CHOICE IS PART OF THE SERVICE RENDERED BY THE ROAD NETWORK. TOLLS FROM THE USERS OF CONGESTED ROADS AT RATES THAT WOULD MEASURE THE COST TO OTHERS CAUSED BY THE AVERAGE ROAD USER WOULD ENCOURAGE BETTER USE OF THE HIGHWAY SYSTEM. THERE ARE OBSERVATIONS ON HOW CLOSELY MAXIMUM EFFICIENCY CAN BE APPROACHED BY PROPER CHOICE OF RATES ON PRESENT TOLL ROADS AND BY OTHER WAYS OF PENALIZING ADDITIONS TO TRAFFIC CONGESTION. BECAUSE OF THE NONLINEAR RELATIONSHIP BETWEEN USE AND COST, SUCH PRICING DOES NOT NECESSARILY PRODUCE REVENUES EQUAL TO THE TOTAL COST OF OPERATING AND FINANCING THE INDIVIDUAL FACILITY. SOME OBSERVATIONS ON THE QUESTION OF CRITERIA FOR EXTENSIONS OR IMPROVEMENTS OF THE NETWORK TO RELIEVE CONGESTION ARE GIVEN. AUTHOR Incidents whose frequencies are unknown cause a large part of the unreliability of road networks. Especially risk-averse road users avoid the possibility of large travel times under a disruption. This paper proposes a traffic assignment which accounts for risk-averseness. Scenarios for all possible disruptions are simulated. The risk-averseness of a user is generalized in a two-parameter route choice formulation. Depending on the parameters, users assign a weight on the travel times for each scenario. This is the basis for their route choice. The simulator can handle a traffic flow composed from different user classes simultaneously in the same network. In the presented case study, the link loads depend on the parameter settings of the users. The route choice for a user class also depends on the risk-averseness of the other users in the network. Vehicular communications are becoming an emerging technology for safety control, traffic control, urban monitoring, pollution control, and many other road safety and traffic efficiency applications. All these applications generate a lot of data which should be distributed among communication parties such as vehicles and users in an efficient manner. On the other hand, the generated data cause a significant load on a network infrastructure, which aims at providing uninterrupted services to the communication parties in an urban scenario. To make a balance of load on the network for such situations in the urban scenario, frequently accessed contents should be cached at specified locations either in the vehicles or at some other sites on the infrastructure providing connectivity to the vehicles. However, due to the high mobility and sparse distribution of the vehicles on the road, sometimes, it is not feasible to place the contents on the existing infrastructure, and useful information generated from the vehicles may not be sent to its final destination. To address this issue, in this paper, we propose a new peer-to-peer (P2P) cooperative caching scheme. To minimize the load on the infrastructure, traffic information among vehicles is shared in a P2P manner using a Markov chain model with three states. The replacement of existing data to accommodate newly arrived data is achieved in a probabilistic manner. The probability is calculated using the time to stay in a waiting state and the frequency of access of a particular data item in a given time interval. The performance of the proposed scheme is evaluated in comparison to those of existing schemes with respect to the metrics such as network congestion, query delay, and hit ratio. Analysis results show that the proposed scheme has reduced the congestion and query delay by 30 with an increase in the hit ratio by 20 . We examine how the selfish behavior of heterogeneous users in a network can be regulated through economic disincentives, i.e., through the introduction of appropriate taxation. One wants to impose taxes on the edges so that any traffic equilibrium reached by the selfish users who are conscious of both the travel latencies and the taxes will minimize the social cost, i.e., will minimize the total latency. We generalize previous results of Cole, Dodis and Roughgarden that held for a single origin-destination pair to the multicommodity setting. Our approach, which could be of independent interest, is based on the formulation of traffic equilibria as a nonlinear complementarity problem by Aashtiani and Magnanti (1981), We extend this formulation so that each of its solutions will give us a set of taxes that forces the network users to conform, at equilibrium, to a certain prescribed routing. We use the special nature of the prescribed minimum-latency flow in order to reduce the difficult nonlinear complementarity formulation to a pair of primal-dual linear programs. LP duality is then enough to derive our results. In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilistic queuing delays. In particular, a reliability measure is defined to characterize extreme events (i.e., when vehicles' queue lengths exceed a predefined threshold with non-negligible probability) using extreme value theory (EVT). Leveraging principles from federated learning (FL), the distribution of these extreme events corresponding to the tail distribution of queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov optimization is used to find the joint transmit power and resource allocation policies for each VUE in a distributed manner. The proposed solution is validated via extensive simulations using a Manhattan mobility model. It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79 reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60 reductions of VUEs with large queue lengths, without an additional power consumption, compared to an average queue-based baseline. Compared to systems with fixed power consumption and focusing on queue stability while minimizing average power consumption, the reductions in extreme events of the proposed method is about two orders of magnitude. To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users. The ubiquitous deployment of mobile and sensor technologies has led to both the capacity to observe human behavior in physical (offline) settings as well as to record it. This provides researchers with a new lens to study and better understand the individual decision processes that were previously unobserved. In this paper, we study decision making behavior of 11,196 taxi drivers in a large Asian city using a rich data set consisting of 10.6 million fine-grained GPS trip records. These records include detailed taxi GPS trajectories, taxi occupancy data (i.e., whether a taxi was occupied with a passenger or was vacant) and taxi drivers’ daily incomes. This capacity to use data where occupancy of the taxi is known is a distinctive feature of our data set and sets this work apart from prior work which has attempted to study driver behavior. The specific decision we focus on pertains to actions drivers take to find new passengers after they have dropped off their current passengers. In particular, we study the role of information derivable from the GPS trace data (e.g., where passengers are dropped off, where passengers are picked up, longitudinal taxicab travel history with fine-grained time stamps) observable by or made available to drivers in enabling them to learn the distribution of demand for their services over space and time. We conduct our study using a heterogeneous Bayesian learning model. We find strong heterogeneity in individual learning behavior and driving decisions, which is significantly associated with individual economic outcomes. Drivers with higher incomes benefit significantly from their ability to learn from not only demand information directly observable in the local market, but also aggregate information on demand flows across markets. Interestingly, our policy simulations indicate information that is noisy at the individual level becomes valuable after being aggregated across various spatial and temporal dimensions. Moreover, the value of information does not increase monotonically with the scale and frequency of information sharing. Finally, our study has important welfare implications in that efficient information sharing leads to an income increase among all drivers, instead of a redistribution of income between different types of drivers. Our work allows us not only to explain driver decision making behavior using these detailed behavioral traces, but also to prescribe information sharing strategy for the firm in order to improve the overall market efficiency. In this paper we investigate the potential benefits of coordinated congestion control for multipath data transfers, and contrast with uncoordinated control. For static random path selections, we show the worst-case throughput performance of uncoordinated control behaves as if each user had but a single path (scaling like log(log(N)) log(N) where N is the system size, measured in number of resources). Whereas coordinated control gives a throughput allocation bounded away from zero, improving on both uncoordinated control and on the greedy-least loaded path selection of e.g. Mitzenmacher. We then allow users to change their set of routes and introduce the notion of a Nash equilibrium. We show that with RTT bias (as in TCP Reno), uncoordinated control can lead to inefficient equilibria. With no RTT bias, both uncoordinated or coordinated Nash equilibria correspond to desirable welfare maximising states. Moreover, simple path reselection polices that shift to paths with higher net benefit can find these states. In this paper, we undertake the first study of statistical multiplexing from the perspective of approximation algorithms. The basic issue underlying statistical multiplexing is the following: in high-speed networks, individual connections (i.e., communication sessions) are very bursty, with transmission rates that vary greatly over time. As such, the problem of packing multiple connections together on a link becomes more subtle than in the case when each connection is assumed to have a fixed demand. We consider one of the most commonly studied models in this domain: that of two communicating nodes connected by a set of parallel edges, where the rate of each connection between them is a random variable. We consider three related problems: (1) stochastic load balancing, (2) stochastic bin-packing, and (3) stochastic knapsack. In the first problem the number of links is given and we want to minimize the expected value of the maximum load. In the other two problems the link capacity and an allowed overflow probability p are given, and the objective is to assign connections to links, so that the probability that the load of a link exceeds the link capacity is at most @math . In bin-packing we need to assign each connection to a link using as few links as possible. In the knapsack problem each connection has a value, and we have only one link. The problem is to accept as many connections as possible. For the stochastic load balancing problem we give an O(1)-approximation algorithm for arbitrary random variables. For the other two problems we have algorithms restricted to on-off sources (the most common special case studied in the statistical multiplexing literature), with a somewhat weaker range of performance guarantees. A standard approach that has emerged for dealing with probabilistic resource requirements is the notion of effective bandwidth---this is a means of associating a fixed demand with a bursty connection that "represents" its distribution as closely as possible. Our approximation algorithms make use of the standard definition of effective bandwidth and also a new one that we introduce; the performance guarantees are based on new results showing that a combination of these measures can be used to provide bounds on the optimal solution.