Measuring the car ownership impact of free-floating car-sharing – A case study in Basel,Switzerland

Free-floating car-sharing schemes operate without fixed car-sharing stations, ahead reservations or return-trip requirements. Providing fast and convenient motorization, they attract both public transport users and (former) car-owners. Thus, their impact on individual travel behavior depends on the user type. Estimating the travel behavior impact of these systems therefore requires quantitative data. Using a two-wave survey approach (shortly after launch of the scheme plus one year later) including travel diaries, this research indicates that (due to their membership) 6% of the free-floating car-sharing customers reduce their private vehicle ownership. Moreover, the results suggest that free-floating car-sharing both complements and competes with station-based car-sharing.     

Roadmap for free-floating bikeshare research and practice in North America

ABSTRACT

The deployment of smartphone-operated, non-station-based bicycle fleets (“dockless” or “free-floating” bikeshare) represents a new generation of bikesharing. Users locate bikes in these free-floating systems using Global Positioning Systems (GPS) and lock bikes in place at their destinations. In this paper, we review current free-floating bikesharing systems in North America and discuss priorities for future research and practice. Since launching in 2017, free-floating bikeshare has expanded rapidly to encompass 200+ systems operating 40,000+ bikes within 150+ cities. In contrast with previous systems, free-floating systems operate almost exclusively using commercial “for-profit” models, amidst concerns of financial sustainability. Governance for these systems is in early stages and can include operating fees, fleet size caps, safety requirements, parking restrictions, data sharing, and equity obligations. We identify research and practice gaps within the themes of usage, equity, sharing resources, business model, and context. While some existing bikesharing literature translates to free-floating systems, novel topics arise due to the ubiquity, fluidity, and business models of these new systems. Systems have numerous obstacles to overcome for long-term sustainability, including barriers common to station-based systems: limited supportive infrastructure, equity, theft or vandalism, and funding. Other unique obstacles arise in free-floating bikeshare around parking, sidewalk right of ways, varied bicycle types, and data sharing. This review offers background in and critical reflection on the rapidly evolving free-floating bikeshare landscape, including priorities for future research and practice. If concerns can be overcome, free-floating bikeshare may provide unprecedented opportunities to bypass congested streets, encourage physical activity, and support urban sustainability.     

Does the aviation Emission Trading System influence the financial evaluation of new airplanes? An assessment of present values and purchase options

In this paper we assess the impact of the CO₂ costs for short- and long-haul aircraft based on present values and on purchase options. We evaluate purchase options with a framework developed for real option analysis to estimate the value of flexibility under uncertain kerosene and CO₂ prices. We find an average influence of CO₂ costs on present values of €1.1 million for the short haul plane and €4.1 million for the long-haul plane over the typical lifetime of an airplane. For purchase options, we find a CO₂ influence of €0.43 million for the long-haul plane and a moderate impact for the short-haul plane. The results underline the importance of CO₂ and kerosene costs for long-haul aircraft.     

A comparison of users’ characteristics between station-based bikesharing system and free-floating bikesharing system: case study in Hangzhou,China

The paper takes station-based bikesharing system (SBS) with docks and dockless free-floating bikesharing system (FBS) as two targets to dig out the relationship between users and use frequency of the services for each scheme, and how the relationship varies from scheme to scheme. To achieve this, studies are carried out focusing on three questions: “who are using these two bicycle services?”; “what are the factors influencing the use frequency of both bicycle systems?”; and “which specific level of the factors influencing the use frequency of both bicycle schemes?” To collect data from users, a survey was designed containing questions for user attributes and service experience and conducted jointly on-line and on-site at four locations with mixed land use in Hangzhou, China. Analysis results show that SBS and FBS have similar user structure but different factors influence use frequency. Based on analysis results, from the user perspective, SBS’s strength is to have good quality with low cost while FBS is more flexible and free to use. Finally, recommendations for SBS are to involve more technology to expand its range to aided bikes for senior citizens and open the access for a mobile renting system, whereas for FBS, it is critical to get government cooperation and for operators to add parking area restrictions into the cellphone application, and create an on-line platform where users can find all the free-floating bike information.

     

Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options

The first analytical stochastic and dynamic model for optimizing transit service switching is proposed for “smart transit” applications and for operating shared autonomous transit fleets. The model assumes a region that requires many-to-one last mile transit service either with fixed-route buses or flexible-route, on-demand buses. The demand density evolves continuously over time as an Ornstein-Uhlenbeck process. The optimal policy is determined by solving the switching problem as a market entry and exit real options model. Analysis using the model on a benchmark computational example illustrates the presence of a hysteresis effect, an indifference band that is sensitive to transportation system state and demand parameters, as well as the presence of switching thresholds that exhibit asymmetric sensitivities to transportation system conditions. The proposed policy is computationally compared in a 24-hour simulation to a “perfect information” set of decisions and a myopic policy that has been dominant in the flexible transit literature, with results that suggest the proposed policy can reduce by up to 72% of the excess cost in the myopic policy. Computational experiments of the “modular vehicle” policy demonstrate the existence of an option premium for having flexibility to switch between two vehicle sizes.     

Dynamic travel time prediction using data clustering and genetic programming

The current state-of-practice for predicting travel times assumes that the speeds along the various roadway segments remain constant over the duration of the trip. This approach produces large prediction errors, especially when the segment speeds vary temporally. In this paper, we develop a data clustering and genetic programming approach for modeling and predicting the expected, lower, and upper bounds of dynamic travel times along freeways. The models obtained from the genetic programming approach are algebraic expressions that provide insights into the spatiotemporal interactions. The use of an algebraic equation also means that the approach is computationally efficient and suitable for real-time applications. Our algorithm is tested on a 37-mile freeway section encompassing several bottlenecks. The prediction error is demonstrated to be significantly lower than that produced by the instantaneous algorithm and the historical average averaged over seven weekdays (p-value <0.0001). Specifically, the proposed algorithm achieves more than a 25% and 76% reduction in the prediction error over the instantaneous and historical average, respectively on congested days. When bagging is used in addition to the genetic programming, the results show that the mean width of the travel time interval is less than 5 min for the 60–80 min trip.     

Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps

Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes.     

Closed-loop Inventory Routing Problem for returnable transport items

Increasing concerns on supply chain sustainability have given birth to the concept of closed-loop supply chain. Closed-loop supply chains include the return processes besides forward flows to recover the value from the customers or end-users. Vendor Managed Inventory (VMI) systems ensure collaborative relationships between a vendor and a set of customers. In such systems, the vendor takes on the responsibility of product deliveries and inventory management at customers. Product deliveries also include reverse flows of returnable transport items. The execution of the VMI policy requires vendor to deal with a Closed-loop Inventory Routing Problem (CIRP) consisting of its own forward and backward routing decisions, and inventory decisions of customers. In CIRP literature, traditional assumptions of disregarding reverse logistic operations, knowing beforehand distribution costs between nodes and customers demand, and managing single product restrict the usage of the proposed models in current food logistics systems. From this point of view, the aim of this research is to enhance the traditional models for the CIRP to make them more useful for the decision makers in closed-loop supply chains. Therefore, we propose a probabilistic mixed-integer linear programming model for the CIRP that accounts for forward and reverse logistics operations, explicit fuel consumption, demand uncertainty and multiple products. A case study on the distribution operations of a soft drink company shows the applicability of the model to a real-life problem. The results suggest that the proposed model can achieve significant savings in total cost and thus offers better support to decision makers.     

Point-to-point speed enforcement systems: Speed limits design criteria and analysis of drivers’ compliance

Point-to-point (P2P) speed enforcement is a relatively new approach to traffic law enforcement. Its technology allows vehicles whose average speed exceeds the speed limit over the controlled section to be fined. It therefore encourages compliance over distances longer than those where spot enforcement policies have been in place.In this paper, a procedure for consistently setting speed limits with such enforcement systems is proposed. Such a method has been applied to design the speed limits on two motorways in the district of Naples, Italy, where P2P enforcement systems became operational in 2009 and 2010. The speed limits, which were set using the Italian geometric design standard to assess vehicle stability and stopping sight distance, have been compared with those provided by using well-known international standards.The impact of the newly designed speed limits and of the P2P enforcement system on drivers’ speeding behaviour has been quantified for each highway section and vehicle type. In fact, accurate measurements of the average travel speeds of each vehicle crossing the enforced sections, before and after the activation of the system, were available. The migration from the old speed limits with spot speed enforcement to the new approach resulted in a notable increase in drivers’ compliance to the speed limits with a remarkable decrease in both the average of individual speeds and in their standard deviation.In addition, the analysis of 3 years of data shows that a gradual adaptation of drivers’ behaviour to the system took place. In particular, a decreasing compliance to the speed limits points to a non-optimal system management. Finally, the results of a revealed preference survey allowed us to make a behavioural interpretation regarding the significantly different impacts measured on the two motorways.     

Improved vehicle classification from dual-loop detectors in congested traffic

Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Length-based vehicle classification from dual loop detectors is among the lowest cost technologies commonly used for collecting these data. Like many vehicle classification technologies, the dual loop approach works well in free flow traffic. Effective vehicle lengths are measured from the quotient of the detector dwell time and vehicle traversal time between the paired loops. This approach implicitly assumes that vehicle acceleration is negligible, but unfortunately at low speeds this assumption is invalid and length-based classification performance degrades in congestion.To addresses this problem, we seek a solution that relies strictly on the measured effective vehicle length and measured speed. We analytically evaluate the feasible range of true effective vehicle lengths that could underlie a given combination of measured effective vehicle length, measured speed, and unobserved acceleration at a dual loop detector. From this analysis we find that there are small uncertainty zones where the measured length class can differ from the true length class, depending on the unobserved acceleration. In other words, a given combination of measured speed and measured effective vehicle length falling in the uncertainty zones could arise from vehicles with different true length classes. Outside of the uncertainty zones, any error in the measured effective vehicle length due to acceleration will not lead to an error in the measured length class. Thus, by mapping these uncertainty zones, most vehicles can be accurately sorted to a single length class, while the few vehicles that fall within the uncertainty zones are assigned to two or more classes. We find that these uncertainty zones remain small down to about 10 mph and then grow exponentially as speeds drop further.Using empirical data from stop-and-go traffic at a well-tuned loop detector station the best conventional approach does surprisingly well; however, our new approach does even better, reducing the classification error rate due to acceleration by at least a factor of four relative to the best conventional method. Meanwhile, our approach still assigns over 98% of the vehicles to a single class.     

Flexibility in planning and development of a container terminal: an application of an American-style call option

The prosperity and social progress of developed and developing economies is highly dependent on the existence of efficient transport infrastructure. Nevertheless, current budgetary constraints are jeopardizing the necessary investments in new or existing infrastructure. New models for planning and managing infrastructure are now necessary to overcome the lack of public economic resources available. Port infrastructure is no exception and, due to the vast number of uncertainties involving these projects, it is relevant to maximize the capture of the latent value of flexible options. Incorporating flexibility in these projects, prior to the implementation phase, can be a solution that allows port managers to address future uncertainties and mitigate risk exposure. This paper analyzes the incorporation of flexibility in port planning through the use of an American call option to the physical capacity expansion problem. The rationale is to implement a flexible expansion plan, through options that can be exercised at any given time, that are able to deal with uncertainty in demand. The paper uses a case study – Terminal Container of Ferrol, in Spain – and the results support the hypothesis that imbedded flexibility will robustly increase the net present value of the project.     

A network-based dispatch model for evaluating the spatial and temporal effects of plug-in electric vehicle charging on GHG emissions

The well-to-wheel emissions associated with plug-in electric vehicles (PEVs) depend on the source of electricity and the current non-vehicle demand on the grid, thus must be evaluated via an integrated systems approach. We present a network-based dispatch model for the California electricity grid consisting of interconnected sub-regions to evaluate the impact of growing PEV demand on the existing power grid infrastructure system and energy resources. This model, built on a linear optimization framework, simultaneously considers spatiality and temporal dynamics of energy demand and supply. It was successfully benchmarked against historical data, and used to determine the regional impacts of several PEV charging profiles on the current electricity network. Average electricity carbon intensities for PEV charging range from 244 to 391 gCO₂e/kW h and marginal values range from 418 to 499 gCO₂e/kW h.     

The effects of activity-travel context and individual attitudes on car-sharing decisions under travel time uncertainty: A hybrid choice modeling approach

People’s daily decision to use car-sharing rather than other transport modes for conducting a specific activity has been investigated recently in assessing the market potential of car-sharing systems. Most studies have estimated transport mode choice models with an extended choice set using attributes such as average travel time and costs. However, car-sharing systems have some distinctive features: users have to reserve a car in advance and pay time-based costs for using the car. Therefore, the effects of activity-travel context and travel time uncertainty require further consideration in models that predict car-sharing demand. Moreover, the relationships between individual latent attitudes and the intention to use car-sharing have not yet been investigated in much detail. In contributing to the research on car-sharing, the present study is designed to examine the effects of activity-travel context and individual latent attitudes on short-term car-sharing decisions under travel time uncertainty. The effects of all these factors were simultaneously estimated using a hybrid choice modeling framework. The data used in this study was collected in the Netherlands, 2015 using a stated choice experiment. Hypothetical choice situations were designed to collect respondents’ intention to use a shared-car for their travel to work. A total of 791 respondents completed the experiment. The estimation results suggest that time constraints, lack of spontaneity and a larger variation in travel times have significant negative effects on people’s intention to use a shared-car. Furthermore, this intention is significantly associated with latent attitudes about pro-environmental preferences, the symbolic value of cars, and privacy-seeking.     

A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment

This paper develops a novel linear programming formulation for autonomous intersection control (LPAIC) accounting for traffic dynamics within a connected vehicle environment. Firstly, a lane based bi-level optimization model is introduced to propagate traffic flows in the network, accounting for dynamic departure time, dynamic route choice, and autonomous intersection control in the context of system optimum network model. Then the bi-level optimization model is transformed to the linear programming formulation by relaxing the nonlinear constraints with a set of linear inequalities. One special feature of the LPAIC formulation is that the entries of the constraint matrix has only {−1, 0, 1} values. Moreover, it is proved that the constraint matrix is totally unimodular, the optimal solution exists and contains only integer values. It is also shown that the traffic flows from different lanes pass through the conflict points of the intersection safely and there are no holding flows in the solution. Three numerical case studies are conducted to demonstrate the properties and effectiveness of the LPAIC formulation to solve autonomous intersection control.     

Towards improved port capacity investment decisions under uncertainty: a real options approach

ABSTRACT

Port activity plays an important role in facilitating international trade. Sufficient capacity is indispensable for a port to attract flows to a region and retain them. The capacity decision is the result of a trade-off between investment and waiting costs. Traditional methods to value expansion projects do not deal adequately with managerial flexibility in the face of uncertainty from different sources in the complex port environment. In this paper, real options (RO) models are identified as an alternative method to making project valuations and investment decisions, as they attribute the correct value to managerial flexibility under uncertainty. In order to be able to build and use such RO models for port capacity investment decisions, the sources and implications of uncertainty in the port and the different RO model specifications need to be understood. To this end, both the literature about uncertainty in the port context and the literature about real options models are reviewed in order to provide researchers who want to build their own decision-making models, with the necessary knowledge of both fields. The review makes clear that the complex interactions in and competition between the logistics chains and their actors coming together in ports have significant impacts on port capacity. Uncertainty is also caused by uncertain international trade flows and changes in legislation following new technologies and environmental impacts. An analysis of the components of some general RO models shows how the options of flexible output, investment size and timing are valued by RO models in a setting with demand uncertainty. Moreover, the review presents researchers with insights in how to deal with cooperative and competitive interactions in the chain, time to build, cyclical markets and legislation changes. It also shows how to value the expansion and the phased investment options. The new insights resulting from this review are subsequently combined in a framework that serves as a guideline to build RO models for port capacity investments. Finally, an exemplifying application of the framework is used to build an actual port capacity investment decision model.     

Simulation based population synthesis

Microsimulation of urban systems evolution requires synthetic population as a key input. Currently, the focus is on treating synthesis as a fitting problem and thus various techniques have been developed, including Iterative Proportional Fitting (IPF) and Combinatorial Optimization based techniques. The key shortcomings of these procedures include: (a) fitting of one contingency table, while there may be other solutions matching the available data (b) due to cloning rather than true synthesis of the population, losing the heterogeneity that may not have been captured in the microdata (c) over reliance on the accuracy of the data to determine the cloning weights (d) poor scalability with respect to the increase in number of attributes of the synthesized agents. In order to overcome these shortcomings, we propose a Markov Chain Monte Carlo (MCMC) simulation based approach. Partial views of the joint distribution of agent’s attributes that are available from various data sources can be used to simulate draws from the original distribution. The real population from Swiss census is used to compare the performance of simulation based synthesis with the standard IPF. The standard root mean square error statistics indicated that even the worst case simulation based synthesis (SRMSE = 0.35) outperformed the best case IPF synthesis (SRMSE = 0.64). We also used this methodology to generate the synthetic population for Brussels, Belgium where the data availability was highly limited.     

Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment

To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers’ behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers’ parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers’ parking behaviour can be incorporated into traffic assignment models and used to support parking systems.     

Uncertainty modeling for bus selection and allocation in a private transportation system

ABSTRACT

This paper describes the development of a probabilistic formulation that provides global optimum selection and allocation of a fleet of buses in a private transportation system of an organization where a third party is hired to provide transportation for its employees and their dependents. In this private transportation system, a fleet of buses is to be selected and allocated to serve employees and their independents on different prescheduled trips along different routes from the organization’s headquarters and residential compound where round-trip times of scheduled trips are subject to uncertainty due to random delays. We propose a probabilistic approach based on 0-1 integer programming for the selection and allocation to determine the optimal number and size of buses assigned to a set of prescheduled trips in a particular time interval. Examples and a case study are presented to illustrate the applicability and suitability of the proposed approach.     

An empirical assessment of the feasibility of battery electric vehicles for day-to-day driving

Driven by sustainability objectives, Australia like many nations in the developed world, is considering the option of battery electric vehicles (BEVs) as an alternative to conventional internal combustion engine vehicles (ICEVs). In addition to issues of capital and running costs, crucial questions remain over the specifications of such vehicles, particularly the required driving range, recharge time, re-charging infrastructure, performance, and other attributes that will be of importance to consumers. With this in mind, this paper assesses (hypothetically) the extent to which current car travel needs could be met by BEVs for a sample of motorists in Sydney assuming a home-based charging set-up, which is likely to be the primary option for early adopters of the technology. The approach uses five weeks of driving data recorded by GPS technology and builds up home-home tours to assess the distances between (in effect) charging possibilities. An energy consumption model based on characteristics of the vehicle, and the speeds recorded by the GPS is adapted to determine the charge used, while a battery recharge function is used to determine charging times based on the current battery level. Among the most pertinent findings are that over the five weeks, (i) BEVs with a range as low as 60 km and a simple home-charge set-up would be able to accommodate well over 90% of day-to-day driving, (ii) however the incidence of tours requiring out-of-home charging increases markedly for vehicles below 24 kWh (170 km range), (iii) recharge time in itself has little impact on the feasibility of BEVs because vehicles spend the majority of their time parked and (iv) effective range can be dramatically impacted by both how a vehicle is driven and use of electrical auxiliaries, and (v) while unsuitable for long, high-speed journeys without some external re-charging options, BEVs appear particularly suited for the majority of day-to-day city driving in big cities where average journey speeds of 34 km/h are close to optimal in terms of maximising vehicle range. The paper has implications for both policy-makers and auto manufacturers in breaking down some of the (perceived) barriers to greater uptake of BEVs in the future.     

Are technology myths stalling aviation climate policy?

Emissions from aviation will continue to increase in the future, in contradiction of global climate policy objectives. Yet, airlines and airline organisations suggest that aviation will become climatically sustainable. This paper investigates this paradox by reviewing fuel-efficiency gains since the 1960s in comparison to aviation growth, and by linking these results to technology discourses, based on a two-tiered approach tracing technology-focused discourses over 20 years (1994–2013). Findings indicate that a wide range of solutions to growing emissions from aviation have been presented by industry, hyped in global media, and subsequently vanished to be replaced by new technology discourses. Redundant discourses often linger in the public domain, where they continue to be associated with industry aspirations of ‘sustainable aviation’ and ‘zero-emission flight’. The paper highlights and discusses a number of technology discourses that constitute ‘technology myths’, and the role these ‘myths’ may be playing in the enduring but flawed promise of sustainable aviation. We conclude that technology myths require policy-makers to interpret and take into account technical uncertainty, which may result in inaction that continues to delay much needed progress in climate policy for aviation.