Comparing the p-median and flow-refueling models for locating alternative-fuel stations

The p-median and flow-refueling models are two of the more popular models for optimal location of alternative-fuel stations. The p-median model, one of the most widely used location models of any kind, locates p facilities and allocates demand nodes to them to minimize total weighted distance traveled. In comparison, the flow-refueling location model (FRLM) is a path-based demand model that locates p stations to maximize the number of trips on their shortest paths that can be refueled. For a path to be considered refuelable, one or more stations must be located on the path in a way that allows the round trip to be completed without running out of fuel, given the vehicle driving range. In this paper, we analyze how well the facilities located by each model perform on the other’s objective function on road networks in Florida. While each objective function degrades somewhat when facilities are located by the other model, the stations located by the flow-refueling model generally do better on the p-median objective than the stations located by the p-median model do on the flow-refueling objective. This difference between the two models is even more pronounced at the state scale than at the metropolitan scale. In addition, the optimal locations for the FRLM tend to be more much more stable as p increases than those located by the p-median model.     

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.     

Measuring accessibility to health care services for older bus passengers: A finer spatial resolution

Health care accessibility is a vital indicator for evaluating areas where there are medical shortages. However, due to the lack of population data with a satisfactory spatial resolution, efforts to accurately measure health care accessibility among older individuals have been hampered to some extent. To address this issue, we attempt to measure accessibility to health care services for older bus passengers in Nanjing, China, using a finer spatial resolution. More specifically, based on one month's worth of bus smart card data, a framework for identifying the home stations (i.e., a passenger's preferred station near their residence) of older passengers is developed to measure the aggregate demand at the bus stop scale. On this basis, a measurement that integrates the Gaussian two-step floating catchment area (2SFCA) and the adjusted 2SFCA methods (referred to as the adjusted Gaussian 2SFCA method) is proposed to measure accessibility to health care services for older people. The results show that: (1) almost all home stations experience inflated demand, especially those located in the suburbs; (2) despite abundant health care resources, home stations in urban districts are rarely identified as high accessibility stations, due to high demand densities among the older population; and (3) more attention should be paid to two types of home stations – those with a medical institution and those with bed shortages, respectively. The first type is predominantly distributed in the periphery of the city, in the suburbs where the travel time required to access the nearest health care service by bus is longer. The second type is mostly located in the outskirts of urban districts and in the central area of one suburb. These findings could help policy makers to implement more appropriate measures to design and reallocate health care resources.     

Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan,China

Many countries have implemented public bike systems to promote sustainable public transportation. Despite the rapid development of such systems, few studies have investigated how built environment factors affect the use of public bikes at station level using trip data, taking account of the spatial correlation between nearby stations. Built environment factors are strongly associated with travel demand and play an important role in the success of public bike systems. Using trip data from Zhongshan's public bike system, this paper employed a multiple linear regression model to examine the influence of built environment variables on trip demand as well as on the ratio of demand to supply (D/S) at bike stations. It also considered the spatial correlations of PBS usage between nearby stations, using the spatial weighted matrix. These built environment variables mainly refer to station attributes and accessibility, cycling infrastructure, public transport facilities, and land use characteristics. Generally, we found that both trip demand and the ratio of demand to supply at bike stations were positively influenced by population density, length of bike lanes and branch roads, and diverse land-use types near the station, and were negatively influenced by the distance to city center and the number of other nearby stations. However, public transport facilities do not show a significant impact on both demand and D/S at stations, which might be attributed to local modal split. We also found that the PBS usage at stations is positively associated with usage at nearby stations. Model results also suggest that adding a new station (with empty capacity) within a 300 m catchment of a station to share the capacity of the bike station can improve the demand-supply ratio at the station. Referring to both trip demand models and D/S models, regression fits were quite strong with larger R² for weekdays than for weekends and holidays, and for morning and evening peak hours than for off-peak hours. These quantitative analyses and findings can be beneficial to urban planners and operators to improve the demand and turnover of public bikes at bike stations, and to expand or build public bike systems in the future.     

Urban public charging station locating method for electric vehicles based on land use approach

Since the use of electric vehicles decreases the local pollution and noise emission electromobility gains a huge potential in sustainable transport. The currently insufficient charging network, namely the low number of stations and ill-chosen locations are a significant barrier of the widespread of electric vehicles in many countries. Accordingly, localization of new charging stations is of utmost importance. In this study, we develop a two-level charging station locating method. Weighted multicriteria methods were introduced to evaluate territory segments and allocate charging stations within a segment applying a hexagon-based approach and using a greedy algorithm. The novelty of the method in comparison with previous studies is that it assesses the potential of electric vehicle use on macro-level and the possible locations of charging stations on micro-level with a focus on the land-use. We apply the method to Hungary (macro evaluation) and to a district of the capital city, Budapest (micro evaluation). It is found that charging stations at P + R facilities, close to concentrated services and high-density areas are better suited to serve urban public charging demand than gas stations.     

A spatial analysis of demand patterns on a bicycle sharing scheme: Evidence from London

This paper investigates the spatial demand for bikesharing through the application of a series of trip generation models for the London Bicycle Sharing Scheme (LBSS). The production of trips from and the arrival of trips at scheme stations are evaluated in reference to how they connect with features of the built environment, demographics of the resident and workplace populations, and attributes of the scheme's structure. A spatial econometrics approach is taken to specify the models, with four different time windows considered throughout the day for all trips taken during 2016. The built environment features show a consistent pattern of results in the model, indicating that proximity to cycling infrastructure, rail stations, parks, university facilities, as well as the density of shops and conventional roads in the vicinity of stations is linked with trip generation rates. The presence of males and Caucasians are associated with higher station demand, aligning with other work on the introduction of new mobility solutions elsewhere, though we do find that greater distances to work tend to depress use. Trip generation is also reduced at the minority of stations located south of the River Thames, indicating that the presence of natural barriers can affect the operation of schemes. The results carry implications for scheme integration in other cities.     

Congestion affecting the dynamic of tourism demand: evidence from the most popular destinations in Spain

In this study, we propose a dynamic econometric model for tourism demand which takes into account the implications of the Tourism Area Life Cycle (TALC) theory on tourism demand. Unlike other dynamic models, in our specification the effect of the lagged demand on the current tourism demand is not constant, but dependent on congestion. We estimate the model using disaggregated data from the most visited Spanish municipalities for the period 2006–2015. Two panel data estimations are carried out: one with the coastal tourist resorts and the other one with the inland municipalities. The results show that tourism congestion reduces the positive previous tourist effect on current arrivals, suggesting that increasing congestion could worsen the attraction of a tourist destination. Congestion is more negatively perceived in inland destinations than coastal ones. Finally, a strong persistence in tourism demand for coastal destinations is shown.     

Integrating node-place and trip end models to explore drivers of rail ridership in Flanders,Belgium

The node-place model is an analytical framework that was devised to identify spatial development opportunities for railway stations and their surroundings at the regional scale. Today, the model is predominantly invoked and applied in the context of ‘transit-oriented development’ planning debates. As a corollary, these model applications share the pursuit of supporting a transition towards increased rail ridership (and walking and cycling), and therefore assumingly a transition to more sustainable travel behavior. Surprisingly, analyses of the importance of node and place interventions in explaining rail ridership remain thin on the ground. Against this backdrop, this paper aims to integrate the node-place model approach with current insights that derive from the trip end modeling literature. To this end, we apply a series of regression analyses in order to appraise the most important explanatory factors that impact rail ridership in Flanders, Belgium, today. This appraisal is based on both geographical and temporal data segmentations, in order to test for different types of railway stations and for different periods of the day. Additionally, we explore spatial nonstationarity by calibrating geographically weighted regression models, and this for different time windows. The models developed should allow policy and planning professionals to investigate the possible demand impacts of changes to existing stations and the walkable area surrounding them.     

Modeling bike-sharing demand using a regression model with spatially varying coefficients

As an emerging mobility service, bike-sharing has become increasingly popular around the world. A critical question in planning and designing bike-sharing services is to know how different factors, such as land-use and built environment, affect bike-sharing demand. Most research investigated this problem from a holistic view using regression models, where assume the factor coefficients are spatially homogeneous. However, ignoring the local spatial effects of different factors is not tally with facts. Therefore, we develop a regression model with spatially varying coefficients to investigate how land use, social-demographic, and transportation infrastructure affect the bike-sharing demand at different stations to address this problem. Unlike existing geographically weighted models, we define station-specific regression and use a graph structure to encourage nearby stations to have similar coefficients. Using the bike-sharing data from the BIXI service in Montreal, we showcase the spatially varying patterns in the regression coefficients and highlight more sensitive areas to the marginal change of a specific factor. The proposed model also exhibits superior out-of-sample prediction power compared with traditional machine learning models and geostatistical models.     

Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system

Recent success of bicycle-sharing systems (BSS) have led to their growth around the world. Not surprisingly, there is increased research towards better understanding of the contributing factors for BSS demand. However, these research efforts have neglected to adequately consider spatial and temporal interaction of BSS station's demand (arrivals and departures). It is possible that bicycle arrival and departure rates of one BSS station are potentially inter connected with bicycle flow rates for neighboring stations. It is also plausible that the arrival and departure rates at one time period are influenced by the arrival and departure rates of earlier time periods for that station and neighboring stations. Neglecting the presence of such effects, when they are actually present will result in biased model estimates. The major objective of this study is to accommodate for spatial and temporal effects (observed and unobserved) for modelling bicycle demand employing data from New York City's bicycle-sharing system (CitiBike). Towards this end, spatial error and spatial lag models that accommodate for the influence of spatial and temporal interactions are estimated. The exogenous variables for these models are drawn from BSS infrastructure, transportation network infrastructure, land use, point of interests, and meteorological and temporal attributes. The results provide strong evidence for the presence of spatial and temporal dependency for BSS station's arrival and departure rates. A hold out sample validation exercise further emphasizes the improved accuracy of the models with spatial and temporal interactions.     

Diversification strategy with random yield suppliers for a mean–variance risk-sensitive manufacturer

We consider the diversification strategy for a mean–variance risk-sensitive manufacturer with unreliable suppliers. We first analyze the linear model and find that the suppliers are selected according to the descending order of their contributed marginal expected profit, and increasing the manufacturer’s risk-averseness leads to a more even allocation of demand across the suppliers. Then, we study the general newsvendor model. By approximating the leftover inventory with a normal distribution, we establish the general properties of the active supplier set and show that the supplier selection rule is similar to that under the risk-neutral setting when the demand uncertainty is large. Moreover, we conjecture that the selection rule also applies when the demand uncertainty is low, which we verify with an extensive numerical study. Our paper makes two contributions: First, we establish the properties of the optimal diversification strategy and develop corresponding insights into the trade off between cost and reliability under the mean–variance framework. Second, we perform comparative statics on the optimal solution, with a particular emphasis on investigating how changes in the supplier’s cost or reliability affect the risk-averse manufacturer’s ordering decisions and customer service level.     

Analysis of the effect of charging infrastructure design on electric taxi driving profiles: A case study approach on the example of Singapore

Replacing conventional vehicle taxis with electric vehicles would be an efficient measure to reduce greenhouse gas emissions. Due to the limited range and long charging times of current battery electric vehicles, it is of utmost importance to provide sufficient charging facilities. This article analyses the impact of the placement and charging power of charging stations on potential mileage and revenue of electric taxis on the example of Singapore. Therefore, we developed an agent-based electric taxi simulation model to investigate electric taxis’ driving profiles with respect to different vehicle types and charging infrastructure designs. This model is also capable of simulating conventional taxi driving profiles. The validation of these simulation results with real taxi data showed that the model is reproducing taxi driving profiles with high accuracy in great detail. We found out that electric taxis could reach the same mileage and revenue as conventional taxis if charging with a power of 160?kW is possible. Furthermore, we discovered that waiting times for available charging stations have a stronger effect on revenue than the length of detours to reach charging stations. Based on these findings, we concluded that it is more important to reduce waiting times by placing sufficient numbers of charging stations at each location before expanding the charging network by installing small numbers of charging stations at many locations.     

Analysis of the potential demand for battery electric vehicle sharing: Mode share and spatiotemporal distribution

Carsharing is considered one of the solutions to urban transport problems. As a new mode in the urban transport system in China, there are still initial questions of how carsharing will perform and what the impacts will be. Accordingly, this study considers battery electric vehicle sharing and investigates its potential demand, with Beijing as the case study. A nested logit model is established and calibrated to analyze mode choice behavior. Further, real trip data is used to estimate the potential demand for battery electric vehicle sharing. In addition, the temporal and spatial distribution of potential demand, the impact of battery electric vehicle sharing on the mode split, and the impact of pricing strategies are analyzed. The results show that an optimistic mode split of battery electric vehicle sharing is 4.23% when the average distance between travelers and stations is 0.5 km. The main source of potential demand is public transport. However, the substitution effect of battery electric vehicle sharing for private vehicles is weak. The potential trips are concentrated in the morning peak period, mainly starting in residential or integrative areas, and ending in commercial areas or green spaces. Commuting and long-distance trips are more sensitive to decreases in price, such that they are more likely to be completed as battery electric vehicle sharing trips. This price decrease could also increase the potential trip ratio during the evening peak period. These findings are useful to governments and operators for implementing policies such as station planning, relocation, and pricing strategies.     

Using machine learning for direct demand modeling of ridesourcing services in Chicago

The exponential growth of ridesourcing services has been disrupting the transportation sector and changing how people travel. As ridesourcing continues to grow in popularity, being able to accurately predict the demand for it is essential for effective land-use and transportation planning and policymaking. Using recently released trip-level ridesourcing data in Chicago along with a range of variables obtained from publicly available data sources, we applied random forest, a widely-applied machine learning technique, to estimate a zone-to-zone (census tract) direct demand model for ridesourcing services. Compared to the traditional multiplicative models, the random forest model had a better model fit and achieved much higher predictive accuracy. We found that socioeconomic and demographic variables collectively contributed the most (about 50%) to the predictive power of the random forest model. Travel impedance, the built-environment characteristics, and the transit-supply-related variables are also indispensable in ridesourcing demand prediction.     

Optimizing shipment,ordering and pricing policies in a two-stage supply chain with price-sensitive demand

In this paper we focus on a two-stage supply chain consisting of one vendor and one buyer. We develop an integrated production–inventory–marketing model to determine the relevant profit-maximizing decision variable values. The model proposed is based on the joint total profit of both the vendor and the buyer, and it finds out the optimal ordering, shipment and pricing policies. We are able to ascertain the optimal decision variable values employing an analytical solution procedure. The numerical evidence suggests that it is more beneficial for the buyer and the vendor to cooperate with each other when the demand is more price sensitive.     

Modelling tourist flow association for tourism demand forecasting

The purpose of this study is to examine tourism demand for Singapore from 1995 to 2013 by six major origin countries which belong to three different regions. Unlike prior tourism research, we take into account the dependence relations among the different tourist flows via copula. Copula is a statistical model of dependence and measurement of association. Specifically, we investigate the association between two tourist flows in each region. Based on empirical copula estimation, the Frank function has been identified as the most appropriate to capture the pairwise dependence structures of tourist flows. The copula-based approach combined with econometric models is proposed for tourism demand analysis that can be used to predict tourist arrivals. We apply the copula-ARDL and copula-ECM frameworks to generate joint forecasts of tourist arrivals from three regions. The findings show that the forecast performance of the Frank copula-based model outperforms the benchmark model which corresponds to the independence structure (no association) of tourist flows.     

Experience conditioning in commuter modal choice modelling – Does it make a difference?

This paper investigates the impact of decision maker’s experience on model elasticities and predicted market share, using data collected in Sydney on commuter mode choice. Usage frequency is used as a proxy for experience and two separate mode choice models are estimated – one with experience conditioning choice and one without. Key model outputs are compared and we find that differences in the value of travel time savings and model elasticities are very marked. This suggests that ignoring experience that one has with each alternative in their choice set may be a candidate source of error in travel demand forecasts. We develop a method to obtain the level of experience for use in application of choice models to increase their prediction power.     

A Linear Programming approach for robust network revenue management in the airline industry

The classical revenue management problem consists of allocating a fixed network capacity to different customer classes, so as to maximize revenue. This area has been widely applied in service industries that are characterized by a fixed perishable capacity, such as airlines, cruises, hotels, etc.It is traditionally assumed that demand is uncertain, but can be characterized as a stochastic process (See Talluri and van Ryzin (2005) for a review of the revenue management models). In practice, however, airlines have limited demand information and are unable to fully characterize demand stochastic processes. Robust optimization methods have been proposed to overcome this modeling challenge. Under robust optimization framework, demand is only assumed to lie within a polyhedral uncertainty set (Lan et al. (2008); Perakis and Roels (2010)).In this paper, we consider the multi-fare, network revenue management problem for the case demand information is limited (i.e. the only information available is lower/upper bounds on demand). Under this interval uncertainty, we characterize the robust optimal booking limit policy by use of minimax regret criterion. We present an LP (Linear Programming) solvable mathematical program for the maximum regret so our model is able to solve large-scale problems for practical use. A genetic algorithm is proposed to find the booking limit control to minimize the maximum regret. We provide computational experiments and compare our methods to existing ones. The results demonstrate the effectiveness of our robust approach.     

A threshold covering flow-based location model to build a critical mass of alternative-fuel stations

To facilitate the transition to alternative-fuel vehicles (AFVs), researchers have developed models for optimally locating an initial refueling infrastructure for AFVs with limited driving range. Recently, clustering of stations has emerged as a strategy to encourage consumers to purchase AFVs by building a critical mass of stations. Clustering approaches, however, have focused on serving demands represented as nodes or arcs rather than origin-destination (O-D) trips. This study proposes a Threshold Coverage extension to the original Flow Refueling Location Model that focuses on the percentage of a zone's O-D trips that can be successfully completed given a typical driving range and location of stations. It is motivated by the idea that drivers in an area will not purchase an AFV unless a critical mass of the trips they regularly make can be completed. Therefore, the new model optimally locates p refueling stations on a network to maximize the sum of weighted demand of covered origin zones, where “covered” means that the zone exceeds a specified threshold percentage of their total outbound round trips that are refuelable. The model is tested on networks for Orlando and the state of Florida. As the threshold percentage is raised, fewer zones can surpass the threshold. Covered nodes increasingly cluster together, as do stations for serving their O-D flows. The model's policy implementation will provide managerial insights for some key concerns of the industry, such as geographic equity vs. critical mass, from a new perspective.