Insights into carsharing demand dynamics: Outputs of an agent-based model application to Lisbon,Portugal

Two important claims for carsharing systems are their increased flexibility and potential contribution to reducing transport externalities such as pollution. Carsharing typically involves a fleet of vehicles in stations around a city that clients may use on an hourly-payment basis. Classical round-trip systems address a niche market of shopping and errand trips. However, a growing market is now arising providing one-way trips to clients. Great uncertainty remains on the economic viability of this type of carsharing given the complex relation between supply and demand, and how this may influence the level of service provided. Realistic modeling tools that include both supply and demand characterization and allow testing several carsharing operational parameters are scarce. In this sense, a detailed agent-based model was developed to simulate one-way carsharing systems. The simulation incorporates a stochastic demand model discretized in time and space and a detailed environment characterization with realistic travel times. The operation includes maintenance operations, relocations and reservations. The model was applied to the case-study city of Lisbon. Our results show that comparing to other modes, carsharing performs worse than private cars both in terms of time and cost. Nevertheless, it clearly outperforms taxis in terms of cost, and outperforms buses, metro and walking in terms of travel time. The competitiveness of carsharing is highly determined by trip length, becoming more competitive than other modes (travel-time wise) as trips become longer. The operational policies as car-fleet relocation and car reservation showed significant effects in enhancing profit while preserving good customers' satisfaction.     

A dynamic carsharing decision support system

This paper proposes a dynamic optimization–simulation model as a decision support system for one-way carsharing organizations. To reduce the vehicle imbalance in one-way systems, a Vehicle Relocation Optimization model is solved successively in a discrete event simulation. Each event is the arrival of a new user. The model is compared to an a priori benchmark model. Autoshare is chosen as a case study. Results show that increasing the reservation time (time between requesting and picking up a vehicle) from 0 to 30 min reduces fleet size by 86%. The model captures a tradeoff between vehicle relocation hours and fleet size.     

Electric and hybrid car use in a free-floating carsharing system

A free-floating carsharing system is a flexible way to provide cars to members in a limited area. This paper discusses the user behavior and choice between electric and hybrid cars in a free-floating system. We use Auto-mobile's transactional and Global Positioning System (GPS) datasets to analyze car use. We develop a binomial logit model to find the probability of choosing an electric car when there is a choice. The analysis reveals that travel distance has a major influence on this probability: electric vehicles are less popular for distances of more than 24 km. Cold temperatures and being a female also decrease the probability, while higher energy level increases odds. The spatial analysis shows a difference in behavior between both car technologies. Trips made by electric cars are less dispersed spatially than those made by hybrid cars.     

Deployment and utilization of plug-in electric vehicles in round-trip carsharing systems

Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs) can reduce gasoline consumption, but increase vehicle acquisition costs and introduce operational constraints. We develop a comprehensive approach to EV/PHEV deployment and utilization in round-trip carsharing systems. First, we formulate and solve the tactical problem of utilizing a mix of gasoline vehicles and EVs/PHEVs to serve trip demand, using Mixed Integer Programming optimization to estimate the minimal gasoline consumption in a computationally efficient manner, and simulation to assess the effect of reservation order on realized gasoline consumption. Second, we use these results to inform the strategic deployment of EVs/PHEVs in the carsharing fleet, using meta-optimization. We implement our approach using data from a large carsharing provider. From the perspective of a carsharing operator, our results suggest that replacing some portion of existing gasoline fleets by EVs/PHEVs would result in gasoline savings likely to outweigh upfront investments and the constraints on vehicle utilization that it creates. Moreover, we find that easily implementable heuristics can capture some of these benefits, and that the integration of vehicle utilization patterns into the design of EV/PHEV deployment strategies can result in added benefits.     

Optimization approach to depot location and trip selection in one-way carsharing systems

In this paper, we present an optimization approach to depot location in one-way carsharing systems where vehicle stock imbalance issues are addressed under three trip selection schemes. The approach is based on mixed-integer programming models whose objective is to maximize the profits of a carsharing organization considering all the revenues and costs involved. The practical usefulness of the approach is illustrated with a case study involving the municipality of Lisbon, Portugal. The results we have obtained from this study provided a clear insight into the impact of depot location and trip selection schemes on the profitability of such systems.     

Shift designs for freight handling personnel at air cargo terminals

This paper presents an integrated mixed integer linear programming (MILP) model for determining manpower requirements and related personnel shift designs for the build-up and break-down of the unit load devices (ULDs) at the air cargo terminal to minimize manpower costs. To utilize the manpower resources efficiently, we implement a new mechanism for demand leveling. In addition, we consider the qualification hierarchy between build-up and break-down workers. A case study based on the real-life data shows that the model is useful for manpower planning at air cargo terminals and the integrated approach is far superior to a traditional two-stage approach.     

Modeling spatio-temporal diffusion of carsharing membership in Québec City

During the last few years, car sharing has undergone significant growth, both in Canada and around the world. In this type of service, users share access to a fleet of vehicles, thereby giving them most of the advantages of automobile use, such as its temporal and spatial flexibility, without many of the constraints of ownership. This study analyzes the geographical and socio-economic factors that favour membership of a carsharing service in Québec City. We combined Cervero’s and Kockelman’s 5D model (density, diversity, design, distance to transit, and destination accessibility) with Hägerstrand’s concept of innovation diffusion so as to analyze the evolution of potential car-sharing membership. Zero-inflated negative binomial (ZINB) regression was used to model the spatial diffusion of the number of car-sharing members in Québec City from 1996 (two years after its inauguration) to 2008 at the local scale, with an annual time step. Results indicate that the carsharing distribution did, indeed, follow Hägerstrand’s innovation diffusion model and that, even though some of the 5D model significantly influenced membership, it was socio-economic factors (education, non-motorization, and family structure) that most greatly affected the membership rate in the service area. The model is used to assess and discuss market coverage potential in Québec City.     

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.     

A carbon footprint analysis of renewable energy technology adoption in the modal substitution of high-speed rail for short-haul air travel in Australia

This study presents a carbon footprint analysis of renewable energy technology adoption in the modal substitution of high-speed rail for short-haul air travel in Australia. The carbon footprint analysis of renewable energy technology adoption in such modal substitution establishes the efficacy of various renewable energy technologies in assisting CO₂ emissions reduction on the Sydney–Melbourne city pair. Renewable energy technology efficacy, captured in the presented low-carbon pathways for high-speed transportation, provides an indication as to the CO₂ emissions reduction potential of the technologies under consideration and their impact upon the environmental performance of the air and high-speed rail modes. These low-carbon pathways displayed a reduction in annual CO₂ emissions throughout the longitudinal period ranging from moderate to significant. Furthermore, this study examines the cumulative effects of the considered renewable energy technologies upon the annual CO₂ emissions production of the high-speed transportation system. A reduction of 56%–69% in annual life cycle CO₂ emissions demonstrated the powerful mitigation potential of a combined air and high-speed rail transportation system in conjunction with renewable energy technologies adoption and a modal shift of 60%. Given this significant mitigation potential, the hybridization of the air and high-speed rail transportation systems with associated renewable energy technologies is considered. The effect of such hybridization would result in the formation of an integrated low-carbon, high-speed transportation system underpinned by the concept of “sustainable mobility” at the national level.     

Environmental footprint of road freight: Case studies from Switzerland

Current Swiss policy aiming to reduce the environmental footprint of heavy vehicles is presented. The environmental footprint of 46 heavy vehicle cases defined as dynamic load, noise, vibration and gaseous emissions is measured based on criteria set by the European project Eureka Logchain footprint. These parameters were measured for freight vehicles selected from the traffic stream and were compared to the criteria set up as a result of current Swiss policy. Results show that parameters that are currently controlled and their reduction encouraged such as gaseous emissions, axle loads and gross weight are indeed below or close to acceptable limits. However, other important parameters such as tyre pressure and noise remain higher than acceptable limits. In order to encourage the operation of vehicles with a low total environmental footprint parameters need to be set and controlled systematically.     

An airline maintenance manpower planning model with flexible strategies

An effective maintenance manpower supply plan not only reduces operating costs but ensures greater aviation safety and punctuality. A variety of flexible management strategies have been widely applied in other industries; however, little research has stressed flexible management strategies for airline crew scheduling problems. Here, we introduce a model that includes various flexible strategies so that an airline can effectively manage its maintenance manpower supply. The model is formulated as a mixed integer program that is solved using a commercial software package, CPLEX. In order to evaluate the model performance, we use the operating data from a leading Taiwan airline.     

Impact of Continuous Climb Operations in ATC workload. Case-study Palma airport

This research assesses the impact of the integration of Continuous Climb operations (CCOs) on Air Traffic Control (ATC) workload. The methodology encompasses different modules: CCO, standard departing and arriving trajectories extracted from an external database, an ad-hoc algorithm for detecting and solving conflicts, and an ATC-workload model with the inclusion of CCO-task modifications. Monte Carlo simulations evaluates different combinations of these modules. Then, a sensitivity analysis is performed to evaluate two parameters: the impact of the calibration of the maximum ATC workload and the percentage increase of the CCO tasks on the ATC workload. The methodology is applied to a case study at Palma airport in Spain. Extensive numerical simulations are executed based on the integration of CCOs into the system from 0% to 100%. The integration of CCOs implies the increase of the ATC workload in the Control Tower (TWR) and the reduction in the Approach Control Centre (APP). The sensitivity analysis shows that the decrease in the increase of CCO-task workload barely affects the number of CCOs that can be operated without exceeding the workload limits. Conversely, the reduction of the ATC workload calibration allows the integration of CCOs around 50% in the case of 90% TWR calibration and up to 100% in the case of 80%.     

Long-term manpower supply planning for air cargo terminals

Four strategic models, combining different manpower supply principles, flexible management strategies and related operating constraints, are developed to help an air cargo terminal manage its long-term manpower supply more effectively. The models are formulated as integer/mixed integer linear programs, which are solved using a mathematical programming solver. To evaluate how the models perform in practice, a case study, utilizing the operating data of a domestic air cargo terminal in Taiwan, is conducted. The preliminary test results show that the four models are efficient and effective, and could be applied to actual operations.     

Simulation method for dispatching national border security manpower to mitigate manpower shortage

Recently, a record number of passengers have entered and exited Taiwan from Taiwan Taoyuan International Airport (TTIA). The number of passengers from Taiwan to mainland China has increased largely because of the improvement of cross-strait cultural interaction and other policies, as well as the opening of direct flights. Not only located in the transport hub of the East Asia Taoyuan International Airport is becoming more important and can provide 24 h Airport service, but also airport immigration officers must take 24 h shift to carry out the passenger document inspection requirement, Immigration officers can be fatigued by long shifts, thus negatively affecting border security clearance efficiency and work performance. Consequently, innovative management practices regarding immigration officers are necessary to strengthen international cooperation against terrorism. This study used system simulation Delphi interviews and a heuristic algorithm to determine the required number of airport immigration officers during a fixed passenger waiting time, for improving the efficiency and stability of airport immigration officers who work to consolidate the border security of the country. The results showed that the utilization of airport immigration officers at Taiwan Taoyuan International Airport is higher than 97.99%, and their work hours have been reduced by more than 54.68%. These results proved that using a system simulation can reduce long work shifts and negligence, which can lead improved border security and airport service quality. Additionally, the simulation results that when the National Immigration Agency and Taiwan Taoyuan International Airport implement the biometrics verification system for noncitizens, significant manpower shortages are expected. These shortages can cause complaints from customers and result in a negative image of the quality of airport service. Assigning priority to setting the E-GATE system to compensate for the lack of immigration officers would result in more efficient and effective border security.     

The interplay between dockless bikeshare and bus for small-size cities in the US: A case study of Ithaca

As another mode of shared transportation, bikeshare can substitute or complement public transit. Prior studies mainly relied on self-reported survey data or aggregated station-level data from docked bikeshare systems, and their conclusions and implications were focused on large cities. It is largely unknown how and to what extent a dockless bikeshare system complements or substitutes public transit, especially in small cities. This study was set to measure the interplay between Lime dockless bikeshare and bus service in Ithaca, NY – a typical small-size college town – and its environs. By joining about 3.42 million records of bus stop data and 102 thousand Lime bikeshare trip data from 2019, two types of Bikeshare-Bus-Linkage (BBL) trips were identified, namely (1) the first-mile trip where a user rides a Lime to board a bus, and (2) the last-mile trip where a user bikes to their destination after alighting a bus. BBL trips were identified using a spatiotemporal proximity framework based on two important parameters: the catchment radius and the time window between a bus stop event and a Lime trip. Different values were tested with a sensitivity analysis, and the parameters were finally set at 100 ft. and 5 min. As such, 3026 BBL trips were identified, which was 3% of total Lime ridership or 0.1% of total bus ridership. Our findings indicated that Lime provided useful first- and last-mile transfers to bus service for commuters. The complementary effect was particularly strong in the urban core and with transit development and employment land use areas. Moreover, in the morning peak, there were more first-mile trips from residential areas to bus stops in the urban core, while in the evening peak more last-mile trips started from bus stops in the urban core to residential areas. Based on the unique first-mile and last-mile trip patterns identified, policy implications and recommendations for bikeshare operators, local government, transit agencies, and transportation policymakers were discussed to better integrate bikeshare and public transit.     

Reliable design of an integrated supply chain with expedited shipments under disruption risks

This paper studied the design of a two-echelon supply chain where a set of suppliers serve a set of terminals that receive uncertain customer demands. In particular, we considered probabilistic transportation disruptions that may halt product supply from certain suppliers. We formulated this problem into an integer nonlinear program to determine the optimal system design that minimizes the expected total cost. A customized solution algorithm based on Lagrangian relaxation was developed to efficiently solve this model. Several numerical examples were conducted to test the proposed model and draw managerial insights into how the key parameters affect the optimal system design.     

Route optimization for energy efficient airport shuttle operations – A case study from Dallas Fort worth International Airport

The objective of this research is to reduce energy consumption from intra airport shuttle operations by optimizing routes and schedules, without compromising on passenger travel experience. To achieve this objective, we propose an optimization model that generates optimal airport shuttle routes for a given set of constraints and a discrete-event simulator that evaluates the optimal shuttle routes in a stochastic environment to understand the tradeoffs between the amount of time passengers wait for shuttles, and shuttle energy consumption. The proposed optimization model and stochastic simulation are tested using shuttle route data provided by the Dallas Fort Worth International Airport. Results indicate that optimized routes can lead to a 20% energy reduction in shuttle operations with a modest 2-min increase in average shuttle wait times. The optimization model and simulator presented here are designed to be generalizable and can be adapted to optimize shuttle operations at any major airport.     

Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice

Modeling travel demand is a vital part of transportation planning and management. Level of service (LOS) attributes representing the performance of transportation system and characteristics of travelers including their households are major factors determining the travel demand. Information on actual choice and characteristics of travelers is obtained from a travel survey at an individual level. Since accurate measurement of LOS attributes such as travel time and cost components for different travel modes at an individual level is critical, they are normally obtained from network models. The network-based LOS attributes introduce measurement errors to individual trips thereby causing errors in variables problem in a disaggregate model of travel demand. This paper investigates the possible structure and magnitude of biases introduced to the coefficients of a multinomial logit model of travel mode choice due to random measurement errors in two variables, namely, access/egress time for public transport and walking and cycling distance to work. A model was set up that satisfies the standard assumptions of a multinomial logit model. This model was estimated on a data set from a travel survey on the assumption of correctly measured variables. Subsequently random measurement errors were introduced and the mean values of the parameters from 200 estimations were presented and compared with the original estimates. The key finding in this paper is that errors in variables result in biased parameter estimates of a multinomial logit model and consequently leading to poor policy decisions if the models having biased parameters are applied in policy and planning purposes. In addition, the paper discusses some potential remedial measures and identifies research topics that deserve a detailed investigation to overcome the problem. The paper therefore significantly contributes to bridge the gap between theory and practice in transport.     

The effect of flight distance on fuel mileage and CO2 per passenger kilometer

In this study, the effect of distance on fuel mileage (fuel burn per nautical mile) and CO₂ intensity, based on a large amount of actual flight data, is discussed for narrow-body commercial aircraft performing domestic flights for flight distances of between ～200 and ～800 NM, in Turkey. For twenty-nine domestic routes, the average CO₂ intensity is calculated to be 88 gr/pa-km, with an 80% load factor, ranging from between 112 gr/pa-km (207 gr/pa-NM) for the shortest route and 78 gr/pa-km (145 gr/pa-NM) for the longest route. An overall runway-to-runway analysis reveals that the average flight fuel consumption increases by 5.1 kg for each additional nautical mile. The discussion is extended to cover wind effect, through westbound and eastbound flights, on fuel consumption. The average fuel mileage of the eastbound flights, above 30 kft, is found to be 11.2% lower than those for the westbound flights. A sensitivity analysis is also carried out to reveal the effects of three main flight performance parameters, namely, cruise altitude, cruise speed and aircraft mass on the cruise CO₂ intensity.     

Air freight hubs in the FedEx system: Analysis of fuel use

This paper provides a data based analysis of FedEx air freighter activities from selected hub locations. The basic idea is that air freighters have a set of range and payload parameters and their corresponding fuel burn depends on weight and distance. Data from 2011 to 12 (FlightAware) are used for 180,000 + flights on origin, destination and aircraft type. The particular aircraft vary widely in payload, but additional parameters may be derived from industry web sites and BTS. The research uses flight activity at hubs such as Memphis and Indianapolis (among others) and computes the aggregate distance flown on specific aircraft. The linkage between the hub and aggregate fuel use (assuming that the out bound flights are allocated to the hub) will give some quantifiable measures of the costs allocated to the hub. The paper examines particular aspects of the air freight system that are especially vulnerable to a spike in the costs of aviation fuel. These observations suggest that traffic to regional air express and air freight hubs is likely to respond in complex ways to fuel costs.