Empty Stations Research Articles

Prepositioning can improve the performance of a dynamic stochastic on-demand public bus system

This paper presents the first application of prepositioning in the context of the dynamic stochastic on-demand bus routing problem (DODBRP). The DODBRP is a large-scale dial-a-ride problem that involves bus station assignment and aims to minimize the total user ride time (URT) by simultaneously assigning passengers to alternative stations and determining optimal bus routes.In the DODBRP, transportation requests are introduced dynamically, and buses are dispatched to stations with known requests. This paper investigates the concept of prepositioning, which involves sending buses not only to currently known requests but also to requests that are likely to appear in the future, based on a given probability.To solve this dynamic and stochastic ODBRP, the paper proposes a heuristic algorithm based on variable neighborhood search (VNS). The algorithm considers multiple scenarios to represent different realizations of the stochastic requests.Experimental results demonstrate the superiority of the prepositioning approach over the DODBRP across various levels of forecast accuracy, lengths of time bucket, and probabilities of realization. Furthermore, the paper shows that removing empty stations as a recourse action can further enhance solution quality. Additionally, in situations with low prediction accuracy, increasing the number of scenarios can lead to improved solutions. Finally, a combination of prepositioning, empty station removal, and the insertion of dynamic requests proves to be effective.Overall, the findings of this paper provide valuable insights into the application of prepositioning in the dynamic stochastic on-demand bus routing problem, highlighting its potential for addressing real-world transportation challenges.

Formation of Fear and Adaptive Behavior in Young Ethnic Minority Women Riding Public Transport

Young ethnic minority women face unique challenges when riding public transport. The present study contributes by providing a narrative-based examination of young ethnic minority women’s formation of fear and adaptive behavior when using public transport. Twenty-nine ethnic minority women, under the age of 30, participated in a semi-structured interview in Auckland, New Zealand. Findings show that adaptive behavior commonly includes pre-planning nighttime trips, relying on their phones to act confident and engage in communication with family en-route, and appearing disinterested to reduce unwanted attention. These measures are expressions of women’s agency and their capacity to prevent victimization and manage fear, particularly in places where they feel unsafe, such as empty stations and dark or enclosed environments. Their lived experiences highlight the level of anxiety they experience on a regular basis. Participants discussed how they are acutely aware of their ethnicity (and appearance) when using public transport at all times of the day. They remain near exits and are constantly on high alert. They also discussed how, from an early age, women are educated in a culture of “stranger-danger” which in the long term erodes their confidence to travel independently and safely. These findings show that the current design of public transport systems does not adequately meet the travel needs of certain groups of women. Public transport authorities are encouraged to gain deeper insights into these issues and develop design strategies which promote inclusiveness.

Selective Polling and Controlled Contention Based WLAN MAC Scheme for Low-Latency Applications

A novel medium access control scheme is proposed and investigated for central coordinated wireless local area networks to support future low-latency applications. The scheme divides each beacon interval into multiple service cycles, and each service cycle is composed of a polling period (PP) and a contention period (CP). By polling stations according to their latest channel accessing times during each PP and properly assigning backoff count for each station during each CP, the proposed scheme reduces the resource waste caused by polling empty stations and collisions, thereby achieving low-latency performance. In each service cycle, the number of stations to poll during the PP is calculated based on station empty probability, and the backoff count assigned to a station that has not been polled during the PP is unique and relatively small. Our simulation results show that the proposed scheme reduces uplink latency effectively, and can afford more traffic under the 0.5 ms uplink latency constraint compared to the conventional schemes.

A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach.

Excess demand prediction for bike sharing systems

One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing. While different operators have different approaches to this functionality, overall it requires a demand-supply analysis of the various stations. While trip data can be used for this task, the existing methods in the literature only capture the observed demand and supply rates. However, the excess demand rates (e.g., how many customers attempted to rent a bike from an empty station) are not recorded in these data, but they are important for the in-depth understanding of the systems’ demand patterns that ultimately can inform operations like rebalancing. In this work we propose a method to estimate the excess demand and supply rates from trip and station availability data. Key to our approach is identifying what we term as excess demand pulse (EDP) in availability data as a signal for the existence of excess demand. We then proceed to build a Skellam regression model that is able to predict the difference between the total demand and supply at a given station during a specific time period. Our experiments with real data further validate the accuracy of our proposed method.

A simulation-optimization algorithm for return strategies in emergency medical systems

In an emergency medical system, the locations of ambulance stations has a direct impact on response time. In this paper, two location models are presented in combination with the hypercube queuing model to maximize coverage probability. In the first model, the locations of free and busy ambulances are considered in the system states, and the hypercube model can be analyzed accurately. The model contains a large number of states, and cannot be used for large-sized problems. For this reason, the second model is presented with the same assumptions as in the first model, except that the locations of busy ambulances are not included in the system state, but approximated based on the arrival rates. Both models are offline and dynamic, in which an ambulance does not necessarily return to the station from which it has been dispatched. Two strategies are defined for returning ambulances to the stations from the customer’s location. In the first strategy, the ambulance is returned to the nearest station after completion of its mission, and in the second strategy, it returns to the empty station that covers the highest demand rate. For evaluation of the performance of the proposed models, small-sized examples are solved for both return strategies using the GAMS software. A simulation-optimization approach combined with a simulated annealing algorithm and a discrete-event simulation are used for solving large-sized problems. Moreover, real data from a case study are used to demonstrate the performance of the models in the real world.

A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

The benefits of having a Bike Sharing System (BSS) in a city are numerous. Among other advantages, it promotes a cleaner environment with less traffic and pollution. One major problem the users of such services encounter is that of full or empty stations, causing user dissatisfaction. The objective of this work is to propose a new user-based incentive method to enhance BSS performance. The proposed method relies on a spatial outlier detection algorithm. It consists of adapting the departure and arrival stations of the users to the BSS state by stimulating the users to change their journeys in view of minimizing the number of full and empty stations. Experiments are carried out to compare our proposed method to some existing methods for enhancing the resource availability of BSSs, and they are performed on a real dataset issued from a well-known BSS called Velib. The results show that the proposed strategy improves the availability of BSS resources, even when the collaboration of users is partial.

Challenges and opportunities in algorithmic solutions for re-balancing in bike sharing systems

In recent years, the booming of the Bike Sharing System (BSS) has played an important role in offering a convenient means of public transport. The BSS is also viewed as a solution to the first/last mile connection issue in urban cities. The BSS can be classified into dock and dock-less. However, due to imbalance in bike usage over spatial and temporal domains, stations in the BSS may exhibit overflow (full stations) or underflow (empty stations). In this paper, we will take a holistic view of the BSS design by examining the following four components: system design, system prediction, system balancing, and trip advisor. We will focus on system balancing, addressing the issue of overflow/underflow. We will look at two main methods of bike re-balancing: with trucks and with workers. Discussion on the other three components that are related to system balancing will also be given. Specifically, we will study various algorithmic solutions with the availability of data in spacial and temporal domains. Finally, we will discuss several key challenges and opportunities of the BSS design and applications as well as the future of dock and dock-less BSS in a bigger setting of the transportation system.

A STOCHASTIC ANALYSIS OF BIKE-SHARING SYSTEMS

As more people move back into densely populated cities, bike sharing is emerging as an important mode of urban mobility. In a typical bike-sharing system (BSS), riders arrive at a station and take a bike if it is available. After retrieving a bike, they ride it for a while, then return it to a station near their final destinations. Since space is limited in cities, each station has a finite capacity of docks, which cannot hold more bikes than its capacity. In this paper, we study BSSs with stations having a finite capacity. By an appropriate scaling of our stochastic model, we prove a mean-field limit and a central limit theorem for an empirical process of the number of stations withkbikes. The mean-field limit and the central limit theorem provide insight on the mean, variance, and sample path dynamics of large-scale BSSs. We also leverage our results to estimate confidence intervals for various performance measures such as the proportion of empty stations, the proportion of full stations, and the number of bikes in circulation. These performance measures have the potential to inform the operations and design of future BSSs.

A new dynamic repositioning approach for bike sharing systems

Repositioning operations are fundamental on bike-sharing systems. Its optimization is necessary in order to achieve the best level of service with minimum agency cost. Literature propose routing models that preventively avoid full and empty stations according to demand forecasting with good results. However, simulations show that reactive methods could improve the performance in some scenarios, because they can adapt to unexpected demand variations quicker. This paper describes a mixed repositioning model for station-based systems that includes both the preventive routing optimization and the real-time reactive adaptability. Results obtained on a simulated case of study (Barcelona ‘Bicing’ system) are positive. Model acts as a flexible repositioning clustering method, which breaks the cluster to control user costs if demand deviates too much from expected.

“Theodora” Way Station: How The Psychological Concept of Life Script Mirrors Neurocognitive Memory of the Future

This article illustrates the relationship between the contemporary understanding of the human capacity for prospection—the representation of possible futures—and the notion of life scripts. Prospection is viewed as a vehicle for transgenerational trauma transmission and as a means of organizing and predicting future experience in line with “memories of the future” based on clients’ historical experience. The authors argue that episcript content and its effects constitute the scene, and the process of scene construction relates to family-specific behaviors. The semantic scaffolding that guides the transmission is related to difficulties in psychosocial functioning. The template representation of the future in the long-term memory of people who are yet to receive the episcript may be represented by a neural network showing minimal activity, which can be compared to an empty station waiting for a train, the train in this instance symbolizing the episcript.

The multi-vehicle stochastic-dynamic inventory routing problem for bike sharing systems

We address the operational management of station-based bike sharing systems (BSSs). In BSSs, users can spontaneously rent and return bikes at any stations in the system. Demand is driven by commuter, shopping, and leisure activities. This demand constitutes a regular pattern of bike usage over the course of the day but also shows a significant short-term uncertainty. Due to the heterogeneity and the uncertainty in demand, stations may run out of bikes or congest during the day. At empty stations, no rental demand can be served. At full stations, no return demand can be served. To avoid unsatisfied demand, providers dynamically relocate bikes between stations in reaction of current shortages or congestion, but also in anticipation of potential future demand. For this real-time decision problem, we present a method that anticipates potential future demands based on historical observations and that coordinates the fleet of vehicles accordingly. We apply our method for two case studies based on real-world data of the BSSs in Minneapolis and San Francisco. We show that our policy outperforms benchmark policies from the literature. Moreover, we analyze how the interplay between anticipation and coordination is essential for the successful operational management of BSSs. Finally, we reveal that the value of coordination and anticipation based on the demand-structure of the BSS under consideration.

BRAVO

Bike sharing systems, which provide a convenient commute choice for short trips, have emerged rapidly in many cities. While bike sharing has greatly facilitated people's commutes, those systems are facing a costly maintenance issue -- rebalancing bikes among stations. We observe that existing systems frequently suffer situations such as no-bike-to-borrow (empty) or no-dock-to-return (full) due to existing ad hoc rebalancing practice. To address this issue, we provide systematic analysis on user trip data, station status data, rebalancing data, and meteorology data, and propose BRAVO - the first practical data-driven bike rebalancing app for operators to improve bike sharing service while reducing the maintenance cost. Specifically, leveraging experiences from two-round round-the-clock field studies and comprehensive information from four data sets, a data-driven model is proposed to capture and predict the safe rebalancing range for each station. Based on this safe rebalancing range, BRAVO computes the optimal rebalancing amounts for the full and empty stations to minimize the rebalancing cost. BRAVO is evaluated with 24-month data from Capital, Hangzhou and NiceRide bikeshare systems. The experiment results show that given the same user demand, BRAVO reduces 28% of the station visits and 37% of the rebalancing amounts.

A Stochastic Analysis of Bike Sharing Systems

As more people move back into densely populated cities, bike sharing is emerging as an important mode of urban mobility. In a typical bike sharing system, riders arrive at a station and take a bike if it is available. After retrieving a bike, they ride it for a while, then return it to a station near their final destinations. Since space is limited in cities, each station has a finite capacity of docks, which cannot hold more bikes than its capacity. In this paper, we study bike sharing systems with stations having a finite capacity. By an appropriate scaling of our stochastic model, we prove a central limit theorem for an empirical process of the number of stations with k bikes. The central limit theorem provides insight on the variance, and sample path dynamics of large scale bike sharing systems. We also leverage our results to estimate confidence intervals for various performance measures such as the proportion of empty stations, the proportion of full stations, and the number of bikes in circulation. These performance measures have the potential to inform the operations and design of future bike sharing systems.

Inventory Routing for Bike Sharing Systems

Bike sharing systems have been launched in many cities. Such systems consist of stations allowing users to rent and return bikes. Due to spatio-temporal variation of requests, stationstend to either be empty or full of bikes. At empty stations, no bikes can be rented while at full stations bikes cannot be returned. The global goal is tosatisfyall rental and return requests anytime at any station. To meet the goal, a fleet of transport vehicles relocates bikes between stations to realize suitable fill levels.We present a multi-periodic inventory routing problem on the operational management level of bike sharing systems. Here, we take into account both time-dependentrequests and target fill levels. The request structureis offered by data analysis. Target fill levels are given by optimization models on the tactical management level. The objective is to minimize the deviation of realizedfill levels andtarget fill levels subjected to capacity and time constraints. Due to the large number of possible solutions, instances are solved by a two-dimensional decomposition approach. First, the given periods are solved independently. Second, in each period the set of stations is divided into disjoint subsets. Each subset is assigned to one vehicle. Appropriate subsets are generated by local search algorithms. Subsets are evaluated by a cost-benefit routing algorithm.For computational studies, we use real-world data of Vienna's bike sharing system “CityBike Wien”. Our results depict that appropriate subsets allow efficient routing and relocation of bikes and thus, target fill levels can be realized.

Anticipatory Service Network Design of Bike Sharing Systems

Today‘s conurbations suffer from inefficiency in transportation systems. Bike sharing systems (BSS) combine the advantages of public and private transportation to better exploit the given transportation infrastructure. They provide bikes for short-term trips at automated rental stations. However, spatio-temporal variation of bike rentals leads to imbalances in the distribution of bikes, causing full or empty stations in the course of the day. Ensuring the reliable provision of bike and free bike racks is crucial for the viability of these systems. On the tactical planning level, target fill levels of bikes at stations are determined to provide reliability in service. On the operational planning level, the BSS operator relocates bikes in vehicles among stations based on target fill levels. A recent approach in tactical service network design (SND) anticipates relocation operations of BSS by means of a dynamic transportation model yielding the required demand of relocation services (RS). A RS is described by pickup and return station, time period, and the number of relocated bikes. RS represent the design decision for implementing a service between two stations in each period for each day of the system operation. The output of the SND model are the time-dependent target fill levels at stations and the set of cost-efficient RS to facilitate these target fill levels. However, the existing approach neglects the sequence of RS into tours, thus leading to a weak anticipation of operational decisions. We extend an existing SND approach by including the concept of service tours (ST). RS are sequenced in ST which start and end at the depot. Experiments shows that the ST obtained by the extended SND yield a stronger anticipation of operational decisions.

Susceptibility of automated external defibrillators to train overhead lines and metro third rails.

Immediate accessibility to automated external defibrillators (AED) is recommended for highly frequented public areas. In train terminals and metro stations electromagnetic interference (EMI) is present. In preparation for a public access defibrillation (PAD) programme in this environment possible effects on AED safety and accuracy were studied. In typical public transportation settings 11 different AED models were bench tested for their sensitivity and specificity of ECG analysis with shockable and nonshockable rhythms provided by an ECG simulator. The devices were exposed to the electromagnetic interference of a rail system operating with 15 kV alternating current (ac) with a frequency of 16 2/3 Hz and a subway system powered with 750 V direct current (dc). AED cables were setup parallel and perpendicular to the tracks, the tests were carried out at 3 m distance from the rails in an empty station and with incoming trains. A total of 5280 tests were recorded, each device was tested a total of 480 times. Fifteen kilovolts 16 2/3 Hz ac interfered more than 750 V dc with the tachyarrhythmia detection systems (P < 0.0001). An AED setup with electrode cables perpendicular to track and power line reduced interference (P < 0.0001), while incoming trains had no significant effect on ECG analysis (P = 0.19). Depending on the AED model, sensitivity ranged from 60 to 100% and specificity from 54 to 100%, representing a positive likelihood-ratio from 1.3 to 241 and a negative likelihood-ratio from 0.7 to 0.0. In the public transportation setting tested, four AED models were unsuitable for automated defibrillation as these devices demonstrated an unacceptable performance in respect of accuracy and safety. In the train setting two devices performed with an accuracy of 57 and 65%. One AED recommended shocks for sinus rhythm at normal frequency. In the metro setting one AED did not advise shocks for ventricular tachycardia. Shock advisory systems of some AED models are susceptible to electromagnetic interference, especially in terminals with 15 kV 16 2/3 Hz ac power supplies. Interference is minimized, if patient position is parallel and electrode cables are perpendicular to overhead line. The choice of AED model for train or metro stations depends on its lack of susceptibility to typical electromagnetic interference.

Synchronous service on a circle

In this paper, we study a synchronous computer network model which may be described as follows. There are n service stations around a circle and there are k people to be serviced with at most one person per station. Time is slotted and in each time slot b adjacent people with an empty station ahead can move synchronously and simultaneously to the next counterclockwise station with probability pb. We show that the stationary distribution is uniform on all possible states, and using the Descartes' rule of signs we find the optimal value of k (fixed p and n) that yields maximal ‘throughput', i.e., the expected number of people served per time slot in equilibrium. We also briefly study an asynchronous model (introduced by D. Sarkar) where in each time slot a person moves with probability p to the next counterclockwise station if it is empty. We find, among other results, the new stationary distribution (Ramakrishnan et al. (1989) also find independently the stationary distribution and they also find the optimal value of k (fixed p and n) that yields maximal throughput in this model). We give tables comparing the synchronous and asynchronous models. Some applications which motivate this study are briefly presented.

Synchronous service on a circle

In this paper, we study a synchronous computer network model which may be described as follows. There are n service stations around a circle and there are k people to be serviced with at most one person per station. Time is slotted and in each time slot b adjacent people with an empty station ahead can move synchronously and simultaneously to the next counterclockwise station with probability pb. We show that the stationary distribution is uniform on all possible states, and using the Descartes' rule of signs we find the optimal value of k (fixed p and n) that yields maximal ‘throughput', i.e., the expected number of people served per time slot in equilibrium. We also briefly study an asynchronous model (introduced by D. Sarkar) where in each time slot a person moves with probability p to the next counterclockwise station if it is empty. We find, among other results, the new stationary distribution (Ramakrishnan et al. (1989) also find independently the stationary distribution and they also find the optimal value of k (fixed p and n) that yields maximal throughput in this model). We give tables comparing the synchronous and asynchronous models. Some applications which motivate this study are briefly presented.

The Merlin-Randell problem of train journeys

The following synchronization problem is studied: “There is a finite set of trains, a finite set of stations, and the engine house. Each station can hold only one train. Each train has a route to follow starting and ending at the engine house. The route may be repeated one or more times. A train may leave a station (or the engine house) only when its immediate destination is an empty station or the engine house. At the beginning all trains are placed at the engine house. The problem is to find a synchronization among train movements which allows parallel movements of trains where possible and enables the completion of each train journey”. A formal model of the train system is proposed assuming that each train route is deterministic and contains no repeated stations. The model is based on the notion of a movement graph being a labeled directed multigraph, the nodes of which represent stations, the arcs of which represent possible train movements. The solution proposed is based on a characterization of inadmissible situations by means of some subgraphs of the movement graph. The properties of these subgraphs, called minimal critical patterns, are investigated. Optimal and suboptimal synchronization strategies are defined. A specification of the optimal strategy in the basic COSY notation is included, and its correctness is demonstrated.