Advertisement

Mobility Patterns in Shared, Autonomous, and Connected Urban Transport

  • Nicole Ronald
  • Zahra Navidi
  • Yaoli Wang
  • Michael Rigby
  • Shubham Jain
  • Ronny Kutadinata
  • Russell Thompson
  • Stephan Winter
Chapter
Part of the Lecture Notes in Mobility book series (LNMOB)

Abstract

A number of recent technological breakthroughs promise disrupting urban mobility as we know it. But anticipating such disruption requires valid predictions: disruption implies that predictions cannot simply be extrapolations from a current state. Predictions have to consider the social, economic, and spatial context of mobility. This paper studies mechanisms to support evidence-based transport planning in disrupting times. It presents various approaches, mostly based on simulation, to estimate the potential or real impact of the introduction of new paradigms on urban mobility, such as ad hoc shared forms of transportation, autonomously driving electrical vehicles, or IT platforms coordinating and integrating modes of transportation.

Keywords

Mobility on demand Demand-responsive transport Ride sharing Mobility as a service Simulated mobility 

1 Introduction

The emerging trend of shared economy in transport has been a major talking point in recent years. The success of companies such as Uber and Lyft has brought the attention of academics and industry alike to dig deeper into this trend [1, 2]. A few studies have shown that sharing economy in the forms of carpooling and ridesourcing has been received positively by some [3, 4]. Convenience, time, and monetary savings have been identified as the major motivations for carpooling; some are willing to experience considerable delays to achieve these benefits [4]. Similarly, the users of ridesourcing services receive the same advantages, if not for the more reliable service (in terms of wait time and accessibility) compared to public transport [3]. Despite the potentials, these sharing economy enabling technologies have brought significant disruption in transportation. Therefore, tools that enable impact prediction of such technologies are important to allow a smooth transition into a more sustainable future.

These trends happen in cities, which are complex systems [5, 6], i.e., systems of nonlinear and nondeterministic behavior. Batty characterizes them as “emergent phenomena generated through a combination of hierarchical levels of decision, driven in decentralized fashion” [5, p. 1042]. Then urban transport is a complex (sub-)system itself: It is a phenomenon emerging from the ‘decentralized’, i.e., uncoordinated mobility of the individuals forming the city’s population. The emergent transport patterns are nonlinear because of capacity limits of transport networks, and they are nondeterministic because of human choice that must rely on the information available to them [7]. The latter point, about behavior of people, is evident already from the prevalent preference for ownership and use of a private car, which from the outside is often irrational from an economic, social, and environmental perspective.

How to predict future states of complex urban systems is therefore a nontrivial task, and one that is approached typically by simulation [5, 8, 9]. Since mobility is a derived demand—not a self-contained system, but derived from the needs of people to access and participate in activities [10, 11]—the starting point for investigating (and predicting) mobility patterns cannot be a random distribution of trips within the geographic area of a city: It has to be bound to the distribution of population, of activities, and of the economic and social characteristics at particular locations. The challenge with potentially disruptive technology is, however, that how this demand is expressed in behavioral choices today does not predict how this demand will be expressed in the future. One (hard) question is whether people’s current choices or preferences are flexible if the offerings of a mobility system change. For example, how many of them, or which group of them are willing to switch to other, novel modes of traveling in order to pursue their activities? The other (hard) question is whether people, with novel choices and perhaps more flexibility in travelling at hand, will also adapt their activities, or activity locations, in order to satisfy their needs. For example, choices by daily routines—picking up a coffee at a particular place—can easily move with changing routines. But even more significant activities can change, for example, if other work places come into reach that were not accessible before.

The current practice to gain insights into people’s choices are surveys. On one hand, travel and activity surveys provide insight into current travel behavior. Traditionally, these are paper-based questionnaires, but also tracking-based surveys are trialed or even applied [12]. In future, surveys—i.e., samples of a population—may be replaced by tracking whole populations, and mining the data for activities [13]. The results are used to break down the aggregate behavior that can be observed from traffic monitoring to an individual level. On the other hand, stated preference surveys [14] try to find out to what degree people are flexible or would change their behavior in the light of new alternatives. Stated preference surveys have been done for example to find out under what parameters people would choose cycling more often as a mode of travelling [15], or what makes people choose airports or airlines [16]. Despite their elaborate theory of design, these surveys suffer from not being able to predict long-term effects: they capture spontaneous reactions of people on choices, but not their learning over time. This means stated preference surveys are valuable where people know the alternatives already, and are less reliable if people are not familiar with the alternatives yet.

This paper investigates to what extent simulation of the complex system of mobility in the city lends itself to predict future states of choices and behavior. These future states (of demand) have direct impact on costs of systems, and thus, on preferences again. Simulation should allow finding the sweet spots in designs, balancing costs and demand before a novel system is implemented, trialed (and in the past, too often failed) or rejected.

This paper will present four examples of analyses and simulations of novel, ad hoc demand-responsive transportation systems (DRT). A particular focus will be on capturing the social, economic and spatial context of mobility in order to come up with valid, i.e., well-grounded results. First, an analysis is presented of how DRT susceptibility can be predicted for a particular region based on experience in other regions. This is then followed by descriptions of three simulations, focusing on comparing different flexible services, comparing fixed route transit and flexible services, and ridesharing with friends. Optimization and the information provided to users also play a role, however, also needs to be adapted for disruptive services. Finally, we conclude regarding the importance of new analytical and simulation approaches for evaluating these emerging systems.

2 Predictions from Experiences

Transportation systems are an important component of urban development and are often of widespread social impact. Hence, before any policy decision making and implementation of transportation services or facilities, it is devisable to predict travel demand patterns and understand travel behavior of the population. Large number of studies have found that various demographic, geographic, social, and economic factors such as household composition, car ownership, employment status, wealth/incomes, age, gender, ethnicity, lifestyles, habits, and preferences can affect travel demand, behavior, and mode choice [17, 18, 19, 20]. The shift to a new mode of transportation from existing modes depends on several factors such as its affordability, travel time, travel cost, convenience, flexibility, technology, and its relative level of service. These factors are not available in advance for an assessment of mode shift as they depend on uptake of the proposed mode in the market.

However, one way to predict the travel demand is by gaining insights from the experience of similar services operating elsewhere in the world. Usability patterns of these services can be studied, and dominant favorable characteristics of users and trips for such transport modes can be identified. Trends of identified characteristics can be found out in the target city using the travel surveys authorities run regularly to collect data for transport planning. These travel surveys record socioeconomic characteristics, demographic characteristics, household attributes and travel details/diaries of a statistically significant and representative sample of the population. In addition to profiling the population they identify the local patterns of travel demand, and thus help to predict the success of a proposed transport mode in the target city.

In order to develop this idea in a formal framework, a review of studies [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36] on DRT services operating in various places in the world had been conducted, and parameters have been identified that favor the use of DRT, such as a clientele in the age groups of 15–24 and 55 or older, a female clientele, a clientele not in workforce or with no driving license, a clientele of low household vehicle ownership, low household income, single person households, a clientele with lack of near train stations, trips for shopping and social reasons, and a clientele with higher trip waiting times or higher trip walking times.

Variations of these parameters can be determined for any target city, using the mentioned travel surveys. In Melbourne this was the Victorian Integrated Survey of Travel and Activity 2009–10 [37]. Typically the parameters have to be aggregated to larger statistical areas, due to privacy or data availability. In our study for Melbourne the aggregation happened at Statistical Area 3 (SA3) for the population census. Such labeled areas can be used to identify the regions that have distinctly higher likelihood to take up DRT, and hence provide better opportunity of implementation of such novel transport services. This methodology has been validated by applying it on an existing mode, in this case public transport and taxi: The results of predicting the susceptibility of certain regions for these modes of transport were compared with the current, observable demand patterns of these modes. A high degree of correspondence suggests that the methodology is suited and can be applied to not yet existing modes of traveling in a target city. In principle, the methodology can also predict the nature of competition among the various existing modes of transportation and a proposed DRT service, and will help in decision-making accordingly.

3 Simulation Applications

3.1 Experimenting with Different DRT Systems

An application of disruptive transportation is the replacement of a fixed route bus service by a partially or non-fixed route service. This can occur when the original service has low patronage and operating costs are relatively high.

In 2013, Public Transport Victoria elected to replace a regional bus service by a service operated by taxis, known as Flexiride [38]. The service has some fixed elements, in that the taxi must always start at the same point at set times during the day. If no one is waiting at that point, nor any bookings have been taken, then the taxi returns to regular service. If there are bookings, the driver decides best how to service those passengers and proceeds. Bookings can only be taken before the taxi leaves the first point.

Data from this new service was used as a base case to experiment with different types of services. A model was built using MATSim, and two scenarios were tested: the existing Flexiride service, along with a fully flexible service, where the constraint on start time and start point were removed [39]. This simulation could then be used to compare the two configurations with respect to the number of successful trips, the average waiting time, and the average excess time. More flexibility, of course, delivers better performance for passengers but higher “costs” in terms of longer travel times or more vehicles needed.

One limitation of this model was that only one day can be simulated, which means that the long-term demand for a flexible service cannot be explored. Another study using the same model analyzed how demand could change over many days based on previous experiences with a flexible service [40]. For example, when deciding to make a trip, the estimated travel time could be used to determine whether the trip is worthwhile. This estimate could be based on personal experiences, for example, how long it usually takes to get from home to the destination, or on global experiences, for example, if others are experiencing delays then you might too.

Another limitation was that only flexible services could be included in the model. This means operation could differ in congested scenarios. However, the use of a multimodal simulation tool such as MATSim means future integration is possible.

The advantage of using simulation in this case is to obtain an idea of how different service configurations will perform with different demand patterns. The ability to build in passenger choice in the future will provide more realistic results.

3.2 Comparing Buses and on-Demand Systems

Public transport operator companies face financial challenges when it comes to providing transit in suburban areas. The main problem is that the demand density is so low that makes it difficult to financially maintain a high frequency public transport and the low frequency is most unattractive to users. Demand-responsive transport (DRT) has promising features to solve this problem. However, there are high financial and operational risks associated with the implementation of a DRT system, which could be foreseen and prevented by the help of simulation. Following preliminary work [41], we use simulation to test our hypothesis that replacing conventional public transport (CPT) with DRT would improve people’s mobility in a small area and in turn solve the problem of underutilized CPT. To this end, the two modes have been compared in term of user performance and cost to the operator.

A limited number of studies have been focused on comparing these modes or finding the demand switch point, which is the highest demand that makes DRT outperform CPT [42, 43, 44, 45]. These studies concluded that DRT outperform CPT in areas with low demand density. Other studies tried to define the demand switch point by analytical methods [46, 47]; however, their studies were focused on DRT operating as feeders.

Two important issues were missing in all these studies: the study of a dynamic ad hoc DRT and considering user preferences. To bridge this gap, this work utilizes an agent-based traffic simulation, MATSim, with an embedded dynamic routing algorithm. This model has the capability of modeling all the passengers as individual agents and offers them ad hoc DRT service to study the impact of replacing CPT with DRT on an individual level. Moreover, the diversity of investigated scenarios helps find the demand switch point.

To test the hypothesis, an extensive comparison was conducted investigating several variations in network shape, demand density and transit frequency. The combination of four transit system (DRT and CPT with three frequencies: 7.5, 15, and 30 min) and five demand density (1, 2, 3, 4, 5, 10 and 15 requests per minute) were investigated in two different network shape: grid and star shape (X) with 16 km2 area.

The scenarios were evaluated in terms of the quality of passengers’ mobility and the operator cost. The quality of the mobility has been defined in this work as the passengers’ perceived travel time and presented as Virtual in-vehicle time (VIVT). The importance of people’s perception of travel time in their decision about their mode of transport has been verified previously [48, 49]. It is calculated according to passengers’ walking time, waiting time, in-vehicle ride time, and number of transfers. The cost to the operator was calculated according to the size of the fleet, the operating hours, and the kilometers driven by each vehicle. In this study, the uptake for both services and their ticket price is assumed to be equal.

The results demonstrate that replacing CPT with DRT results in a significant improvement in people’s mobility. The VIVT is the lowest for DRT users in all scenarios, which shows the superiority of this mode in terms of user performance. Moreover, the percentage of people waiting less than 10 min has been calculated for all scenarios. The average percentage for CPT users is 99% in both networks, while it goes up to 70 and 80% in star shape and grid network respectively.

After verifying that replacing DRT with CPT results in a better situation for users, it is necessary to evaluate their cost. DRT’s cost is less than any frequency of CPT in grid network and the demand switch point can be defined according to the frequency. However, the highest demand for which DRT can outperform CPT (in terms of cost) is almost 7 requests per minute.

As a conclusion, replacing CPT with DRT results in improvement in people’s mobility in small areas with low demand, mostly without any extra cost to the operator. This means that this new system can solve the problem of uneconomical transit services for the operators and provide a better mobility option for inhabitants. A high quality, door-to-door public transport service also improves the social equity of a suburb for its habitants and increase the standard of living. The fact that the operator cost depends on the demand in DRT operation demonstrate its flexibility, which is very useful in adapting the supply to demand in different time periods. This work demonstrated the power of simulation tools to evaluate the performance of a new system and compare it to old ones on a limited time scenario. However, it is expected that by developing more complex models, it is also possible to study the DRT performance on a longer run (for instance over a week or a month).

3.3 Motivating Behavioral Change

Human mobility and travel decision-making are complex behaviors that are affected not only by physical conditions, e.g., space-time limits, but also by sociopsychological factors. Especially for such collaborative behaviors with multiple participants as ridesharing, decisions are made in many aspects. Trajectory analysis has indicated that many people could do ridesharing according to their space-time concurrence [50]. But despite the environmental and economic benefits, there is still a low rate in participation of ridesharing [51, 52, 53]. The contradiction is partially explained by some surveys showing that the willingness to share rides with social contacts (a first or second degree socially connected person, hereafter called “friend”) is significantly higher than with strangers [52, 53]. Therefore, friendship can be a good drive for ridesharing. While the low willingness to share rides with strangers signifies that many of the existing rides are in fact invalid for a certain person, ridesharing exclusively with friends and declining offers from strangers nearby may lead to higher detour cost and even less opportunities to get a ride within given space-time budgets.

To prove the benefit of ridesharing with friends, two null hypotheses against the objective are to be rejected. The first one is that sharing rides only with friends significantly increases detour cost. The second is that the number of successfully matched rides is significantly lower with friends than with anyone. Detour cost and matching rate are influenced by social similarity and spatial distribution of friends. Social similarity contributes to the willingness to share rides with and detour tolerance for a person, while spatial distribution decides detour cost.

Agent-based microsimulation provides a way to cope with the complex behavior process. An agent-based transport simulation is set up using NetLogo assuming that everyone in the study area has a car. The simulation measures the reduction of the total amount of cars by changing the behaviors of private car owners. Based on a regular gridline road network with artificially generated origin/destination points of trips, the model systematically tests different parameters to reach the general conclusions applicable to different urban contexts. The parameters include social network structure (average degree of friendship), spatial distribution of friends (spatially clustered vs. random), and varied tolerance and willingness for different social levels (i.e., direct friends, indirect friends, and strangers). Two populations with small world social network structures are simulated, with 2000 and 5000 agents respectively. There are three matching patterns: (1) any driver and passenger must be direct friends, (2) any driver and passenger must be either direct or indirect friends, and (3) no one has to be friends.

The detour costs of the cross categories of social network structures and matching patterns are calculated for statistical analysis. The analysis contributes three major findings. First, detour cost with friends is not necessarily significantly higher than collaboration with anyone. Especially when friendships are spatially clustered, sharing with friends saves more. Second, even if not excluding strangers, giving priority to friends drastically increases successful matching between friends. Finally, a successful matching rate is not positively associated with the size of choice set. It also depends on the spatial distribution of friendship.

3.4 Using Simulations to Test Optimization Strategies

In order for a DRT service to operate efficiently, an important aspect to consider is the optimization of the vehicle routes. By carefully planning the vehicle routes, the operating cost can be minimized while still guaranteeing a certain level of service quality. Thus, the main use of the optimization is to determine the “sweet spot” of the trade-off between service quality and cost, which eventually leads to an economically justifiable DRT service framework.

The routing problem encountered in a demand-responsive transport is categorized as a Pick-up and Drop-off Problem with Time Windows (PDPTW), where each customer has a load to be picked up and dropped off within the nominated time windows. Occasionally, late arrivals (outside of the time windows) are allowed with some penalties being accrued. The PDPTW is typically considered to be a static problem with customer requests being fixed in advance. A number of solvers have been developed for the PDPTW, including exact solution methods [54, 55, 56, 57, 58] and heuristic algorithms [59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]. When the scale of the problems increased and more layers of complexity are added into the problem, most exact solution methods are computationally expensive and, thus, implementation usually relies on heuristic algorithms [71].

The inevitable challenge when implementing most kinds of heuristic algorithms is the fine-tuning of their parameters. Arguably, the performance of these algorithms is heavily dependent on the selection of these parameters, and a set of parameters can be good for one scenario but not the others. One of the benefits of using a simulation platform is the ability to fine-tune the selection of these parameters prior implementation. In most cases, the methods used to fine-tune these parameters require iterations of specific conditions, which are impossible to perform in practice.

To illustrate this point, an example will be drawn from the algorithm discussed in [72]. The considered algorithm is a two-layered neighborhood search. At the bottom layer, the neighborhood algorithm is used to optimize the route of a vehicle given a certain set of trip requests to be served. At the top layer, the passenger allocation is optimized using another neighborhood search algorithm, requiring multiple call of the lower layer optimization.

In the proposed algorithm, there are two parameters at each layer, defining the neighborhood size and the number of iterations. At each iteration, the algorithm first randomly chooses and evaluates the costs of a number of solutions that are considered as the “neighbors” of the solution at the current iteration. Then, the best neighbor is defined as the solution for the next iteration (this is done even though the best neighbor is worse than the solution of current iteration, which is useful to avoid being trapped at a local extremum) and the process is repeated multiple times according to the predefined number of iterations. The detail of the algorithm is outlined in [72].

The optimization result when using this algorithm heavily depends on these parameters. With higher neighborhood size and number of iterations, it is expected that the algorithm would asymptotically approach the best possible result, at the cost of an increasingly heavy computational burden. Therefore, it is of the operator’s interest to select a set of parameters that provides an excellent optimization result with a minimal computational time.

In order to do this, a pilot study can be carried out in a simple simulation platform. As an example, the pilot study is carried out by considering a single vehicle case serving a total of 30 trip requests in a certain predefined network. Figure 1 shows, firstly, the optimization result for various parameters values (note that the neighborhood size is obtained by multiplying the neighborhood size factor and the number of stops in the route, i.e., 60) is shown and, secondly, a similar plot for the total wall time is produced. By superimposing the contour maps of both graphs, it is possible to determine a pair of values for these parameters that produces the best result while still minimizing the wall time of the optimization (in this example, via visual observation). Thus, the pilot study enables a convenient way to determine the optimal parameter values, which otherwise is impossible to obtain in practice.
Fig. 1

Results of the fine-tuning of the neighborhood search algorithm

The benefit of using a simulation platform for routing optimization is not limited to the illustrated example. Another important aspect is to decide which algorithms to use. This is especially important in a dynamic vehicle routing problem, where heuristic is the prevalent approach. For instance, in the dynamic case, an optimal solution might become relatively of poor quality when new ad hoc requests emerge. Therefore, many suggest exploiting some known stochastic information about the demand [73, 74, 75, 76]. A comparison study can be easily carried out by using a simulation platform. Therefore, this again shows the importance of a simulation platform when deciding the most suitable optimization algorithm in a DRT service.

4 The Role of User Interfaces

Overall, this paper is addressing the impact of a novel mobility service on people’s modal choices. But the acceptance in the market does in the end not only depend on the mobility options themselves, but also on the information, and particularly the ease of access to this information about the use of a service. For example, simple payment options can make a service more attractive.

Especially services operating on a demand-responsive basis have to consider the interaction with their users. These users do have to express their demand on the fly, and then to choose from the offerings of the service. This dialog has to happen in the most convenient and intuitive way, which is not trivial from two perspectives. One is the communication about places and times in the world, a known usability challenge for all travel planners. The other is the real-time aspect of this negotiation in ad hoc demand-responsive services.

For example, a service may use predictions of demand in order to balance supply and demand as in Sect.  3.4, but in unexpected situations of poor service, such as during times of high demand or low supply, it may be impossible for a system to satisfy some requests. In such situations, a person would require intuitive information to inform their travel planning alternatives. Here subtle changes to a trip’s constraints, that is, flexibility in space or time, may increase the success of getting a ride. For this reason a graphical, map-based user interface that communicates potential pick-up locations by matching a drop-off (Fig. 2) may be used for situation awareness [77]. Such an interface to spatial information may also be used to motivate behavioral change: Compromises in space-time may actually yield greater individual utility due to a range of other affordances like physical exercise or carbon emissions [78].
Fig. 2

The ease of use of travel service interfaces can be deciding on market acceptance (base layer © Google Maps 2016)

5 Conclusions and Outlook

This paper collects various approaches to predict human behavior when novel and disruptive forms of transport are added to the complex system of urban mobility. In each case the focus is on long-term impact, not preconceived conceptions. In particular simulation permits the exploration of the detailed operation of disruptive services, and thus also helps understanding the operator emergent patterns prior to the introduction of any new service. As the studied ‘disruptive’ transportation services are flexible, either in route or time or both, it is more difficult to predict how they will perform compared to scheduled services along given routes. The simulation has to solve the vehicle’s scheduling and routing problem, and additionally consider the social, economic and spatial context of people and their mobility demand in order to come to valid conclusions. This paper has put a special effort in explaining how each model has considered this context. It is this context that determines emergent patterns. Without a particular care for this capturing and modeling of the relevant factors of this context the results of simulations would be as random as their assumptions on population, demand or acceptance of pricing. For example, the prediction method based on use patterns (Chapter “ Multimodal Transportation Payments Convergence—Key to Mobility”) had been successfully validated by applying the same methodology not only to a novel service, but also to an already existing service in a known environment. Since the predicted success of the existing service in the particular area matched the actual usage sufficiently, the methodology can also deliver trustable results for the novel service in the same area.

Furthermore, agent-based microsimulation allows for deep modeling of the individuals’ preferences and decision making, which then enables to study this aspect in itself, due to the full control of parameters in such experiments (in contrast to the real world). Expanding our studies in this direction is part of future work. It will include factors such as ad hoc mode choice or modeling at which time in advance individuals make a request for ad hoc transport demand. Again, the challenge will be to identify the relevant parameters for such detailed modeling of behavior and choice.

Simulation as a tool, and context awareness as an indispensable part of a simulation model, is also suited to consider not only single modes for their potential to impact (and potentially disrupt) people’s behavior and choices, but integrate these novel modes with other modes and study bigger pictures.

Notes

Acknowledgements

The authors acknowledge support through the Australian Research Council (LP120200130).

References

  1. 1.
    Nicas, J.: Google takes on uber with new ride-share service. 31 August 2016. www.wsj.com/articles/google-takes-on-uber-with-new-ride-share-service-1472584235. Accessed 1 Sept 2016
  2. 2.
    Shaheen, S.: Mobility and the sharing economy (editorial). Transport Policy (2016)Google Scholar
  3. 3.
    Rayle, L., et al.: Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transp. Policy 45, 168–178 (2016)CrossRefGoogle Scholar
  4. 4.
    Shaheen, S., Chan, N., Gaynor, T.: Casual carpooling in the San Francisco Bay area: understanding user characteristics, behaviors, and motivations. Transport Policy (2016)Google Scholar
  5. 5.
    Batty, M.: Cities as complex systems: scaling, interaction, networks, dynamics and urban morphologies. In: Meyers, R.A., (ed.) Encyclopedia of Complexity and Systems Science, vol. 1041–1071. Springer: New York, NY, (2009)Google Scholar
  6. 6.
    Batty, M., et al.: Entropy, complexity, and spatial information. J. Geogr. Syst. 16(4), 363–385 (2014)CrossRefGoogle Scholar
  7. 7.
    Simon, H.A.: Bounded rationality, in utility and probability. In: Eatwell, J., Milgate, M., Newman, P. (eds.) pp. 15–18. The Macmillan Press Ltd, New York, (1990)Google Scholar
  8. 8.
    Benenson, I., Torrens, P.M.: Geosimulation: Automata-based Modeling of Urban Phenomena. Wiley, Chichester, UK (2004)CrossRefGoogle Scholar
  9. 9.
    Torrens, P.M.: Geosimulation, automata, and traffic modelling. In: Hensher, D.A., et al. (eds.) Handbook of Transport Geography and Spatial Systems pp. 549–564. Elsevier: Amsterdam (2004)Google Scholar
  10. 10.
    Mokhtarian, P.L., Salomon, I.: How derived is the demand for travel? Some conceptual and measurement considerations. Transp. Res. Part A 35(8), 695–719 (2001)Google Scholar
  11. 11.
    Axhausen, K.W., Gärling, T.: Activity-based approaches to travel analysis: conceptual frameworks, models, and research problems. Transport Rev. 12(4), 323–341 (1992)CrossRefGoogle Scholar
  12. 12.
    Cottrill, C., et al.: Future Mobility Survey. Transport. Res. Record: J. Transport. Res. Board 2354, 59–67 (2013)CrossRefGoogle Scholar
  13. 13.
    Scholz, R.W., Lu, Y.: Detection of dynamic activity patterns at a collective level from large-volume trajectory data. Int. J. Geogr. Inf. Sci. 28(5), 946–963 (2014)CrossRefGoogle Scholar
  14. 14.
    Louviere, J.J., Hensher, D.A., Swait, J.D.: Stated Choice Methods: Analysis and Applications. Cambridge University Press, Cambridge, UK (2000)CrossRefzbMATHGoogle Scholar
  15. 15.
    Tilahun, N.Y., Levinson, D.M., Krizek, K.J.: Trails, lanes, or traffic: valuing bicycle facilities with an adaptive stated preference survey. Transp. Res. Part A: Policy Pract. 41(4), 287–301 (2007)Google Scholar
  16. 16.
    Hess, S., Adler, T., Polak, J.W.: Modelling airport and airline choice behaviour with the use of stated preference survey data. Transp. Res. Part E: Logistics Transp. Rev. 43(3), 221–233 (2007)CrossRefGoogle Scholar
  17. 17.
    Contrino, H., McGuckin, N.: Demographics matter travel demand, options, and characteristics among minority populations. Public Works Manage. Policy 13(4), 361–368 (2009)CrossRefGoogle Scholar
  18. 18.
    Kattiyapornpong, U., Miller K.E.: Understanding travel behavior using demographic and socioeconomic variables as travel constraints. In: ANZMAC 2006: Advancing theory, maintaining relevance: Proceedings of the 2006 Australian & New Zealand Marketing Academy Conference: [Queensland University of Technology, School of Advertising, Marketing and Public Relations] (2006)Google Scholar
  19. 19.
    Litman, T.: Understanding Transport Demands and Elasticities (2013)Google Scholar
  20. 20.
    Rasouli, S., Timmermans, H.: Applications of theories and models of choice and decision-making under conditions of uncertainty in travel behavior research. Travel Behav. Soc. 1(3), 79–90 (2014)CrossRefGoogle Scholar
  21. 21.
    ActiveAge, An introduction to Demand Responsive Transport as a Mobility Solution in an Ageing Society, 2008Google Scholar
  22. 22.
    Anspacher, D., Khattak, A.J., Yim, Y.: Demand-responsive transit shuttles: who will use them? Calif. Partners Adv. Transit Highways (PATH) (2004)Google Scholar
  23. 23.
    Bearse, P., et al.: Paratransit demand of disabled people. Transp. Res. Part B: Methodol. 38(9), 809–831 (2004)CrossRefGoogle Scholar
  24. 24.
    Enoch, M.P., et al.: Evaluation study of demand responsive transport services in Wiltshire. Final report, Loughborough University, Loughborough (2006)Google Scholar
  25. 25.
    Häme, L.: Demand-responsive transport: models and algorithms. In: Department of Mathematics and Systems Analysis. Aalto University, Aalto (2013)Google Scholar
  26. 26.
    Koffman, D.: Operational experiences with flexible transit services, vol. 53. Transportation Research Board (2004)Google Scholar
  27. 27.
    Laws, R.: Evaluating Publicly-Funded DRT Schemes in England and Wales, Loughborough University (2009)Google Scholar
  28. 28.
    Lerman, S.R., et al.: A model system for forecasting patronage on demand responsive transportation systems. Transp. Res. Part A: General 14(1), 13–23 (1980)CrossRefGoogle Scholar
  29. 29.
    Maddern, C., Jenner, D.: Telebus mobility and accessibility benefits: final report. In 12th International Conference on Mobility and Transport for Elderly and Disabled transport (TRANSED): Hong Kong (2007)Google Scholar
  30. 30.
    Mageean, J., Nelson, J.D.: The evaluation of demand responsive transport services in Europe. J. Transp. Geogr. 11(4), 255–270 (2003)CrossRefGoogle Scholar
  31. 31.
    Nelson, J.D., Phonphitakchai, T.: An evaluation of the user characteristics of an open access DRT service. Res. Transp. Econ. 34(1), 54–65 (2012)CrossRefGoogle Scholar
  32. 32.
    Rosenbloom, S., Fielding, G.J.: Transit Markets of the Future: The Challenge of Change, vol. 28. Transportation Research Board (1998)Google Scholar
  33. 33.
    Ryley, T.J., et al.: Developing Relevant Tools for Demand Responsive Transport (DRT). In: ATCO Conference, Liverpool (2013)Google Scholar
  34. 34.
    Scott, R.: Demand Responsive Passenger Transport in Low-Demand Situations December 2010 (2010)Google Scholar
  35. 35.
    Spielberg, F., Pratt, R.H.: Demand-Responsive/ADA-Traveler Response to Transportation System Changes (2004)Google Scholar
  36. 36.
    Wang, C., et al.: Multilevel modelling of demand responsive transport (DRT) trips in greater Manchester based on area-wide socio-economic data. Transportation 41(3), 589–610 (2014)CrossRefGoogle Scholar
  37. 37.
    Victorian Integrated Survey of Travel & Activity 2009–10, Survey Procedures and Documentation, The Victorian Department of Transport (2011)Google Scholar
  38. 38.
    Public Transport Victoria, New PTV FlexiRide service for Yarrawonga and Mulwala: Melbourne, Australia, 2 (2013)Google Scholar
  39. 39.
    Ronald, N., Thompson, R.G., Winter, S.: A comparison of constrained and ad-hoc demand-responsive transportation systems. Transp. Res. Rec. 2536, 44–51 (2015)CrossRefGoogle Scholar
  40. 40.
    Ronald, N., Thompson, R.G., Winter, S.: Modelling ad-hoc DRT over many days: a preliminary study. In: 21st International Congress on Modelling and Simulation (MODSIM): Gold Coast, Qld, Australia, pp. 1175–1181 (2015)Google Scholar
  41. 41.
    NavidiKashani, Z., Ronald, N., Winter, S.: Comparing demand responsive and conventional public transport in a low demand context. In: First International Workshop on Context-Aware Smart Cities and Intelligent Transport Systems, Sydney (2016)Google Scholar
  42. 42.
    Daganzo, C.F.: Checkpoint dial-a-ride systems. Transp. Res. Part B: Methodol. 18(4–5), 315–327 (1984)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Diana, M., Quadrifoglio, L., Pronello, C.: Emissions of demand responsive services as an alternative to conventional transit systems. Transp. Res. Part D: Transport Environ. 12(3), 183–188 (2007)CrossRefGoogle Scholar
  44. 44.
    Diana, M., Quadrifoglio, L., Pronello, C.: A methodology for comparing distances traveled by performance-equivalent fixed-route and demand responsive transit services. Transp. Plann. Technol. 32(4), 377–399 (2009)CrossRefGoogle Scholar
  45. 45.
    Edwards, D., Watkins, K.: Comparing fixed-route and demand-responsive feeder transit systems in real-world settings. Transp. Res. Record: J. Transp. Res. Board 2352, 128–135 (2013)CrossRefGoogle Scholar
  46. 46.
    Chang, S., Yu, W.J.: Comparison of subsidized fixed-and flexible-route bus systems. Transp. Res. Record: J. Transp. Res. Board 1557, 15–20 (1996)CrossRefGoogle Scholar
  47. 47.
    Quadrifoglio, L., Li, X.: A methodology to derive the critical demand density for designing and operating feeder transit services. Transp. Res. Part B: Methodol. 43, 922–935 (2009)CrossRefGoogle Scholar
  48. 48.
    Beirão, G., Sarsfield, J.A.: Cabral, Understanding attitudes towards public transport and private car: A qualitative study. Transp. Policy 14(6), 478–489 (2007)CrossRefGoogle Scholar
  49. 49.
    Hensher, D.A., Stopher, P., Bullock, P.: Service quality—developing a service quality index in the provision of commercial bus contracts. Transp. Res. Part A: Policy Pract. 37(6), 499–517 (2003)Google Scholar
  50. 50.
    Santi, P., et al.: Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. 111(37), 13290–13294 (2014)CrossRefGoogle Scholar
  51. 51.
    Amey, A.M.: Real-time ridesharing: exploring the opportunities and challenges of designing a technology-based rideshare trial for the MIT community, Massachusetts Institute of Technology (2010)Google Scholar
  52. 52.
    Chaube, V., Kavanaugh, A.L., Perez-Quinones, M.A.: Leveraging social networks to embed trust in rideshare programs. In: System Sciences (HICSS), 2010 43rd Hawaii International Conference on: IEEE (2010)Google Scholar
  53. 53.
    Wessels, R.: Combining Ridesharing and Social Networks, UTwente (2009)Google Scholar
  54. 54.
    Baldacci, R., Bartolini, F., Mingozzi, A.: An exact algorithm for the pickup and delivery problem with time windows. Oper. Res. 59, 414–426 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  55. 55.
    Desrosiers, J., Dumas, Y., Soumis, F.: A dynamic programming solution of the large-scale single-vehicle dial-a-ride problem with time windows. Am. J. Math. Manage. Sci. 6, 301–325 (1986)zbMATHGoogle Scholar
  56. 56.
    Psaraftis, H.N.: Scheduling large-scale advance-request dial-a-ride systems. Am. J. Math. Manage. Sci. 6, 327–367 (1986)zbMATHGoogle Scholar
  57. 57.
    Zhou, J.: Routing by mixed set programming. In: Proceedings of the 8th International Symposium on Operations Research and Its Applications, pp. 155–166 (2009)Google Scholar
  58. 58.
    Ropke, S., Cordeau, J.F.: Branch and cut and price for the pickup and delivery problem with time windows. Transp. Sci. 43, 267–286 (2009)CrossRefGoogle Scholar
  59. 59.
    Badaloni, S., et al.: Addressing temporally constrained delivery problems with the swarm intelligence approach. Intell. Auton. Syst. 10, 264–271 (2008)Google Scholar
  60. 60.
    Bent, R., Hentenryck, P.V.: A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows. Comput. Oper. Res. 33, 875–893 (2006)CrossRefzbMATHGoogle Scholar
  61. 61.
    Gronalt, M., Hartl, R.F., Reimann, M.: New savings based algorithms for time constrained pickup and delivery of full truckloads. Eur. J. Oper. Res. 151, 520–535 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  62. 62.
    Hasle, G., Kloster, O.: Industrial vehicle routing. In: Hasle, G., Lie, K.A., Quak, E. (eds.) Geometric Modelling, Numerical Simulation, and Optimization, pp. 397–435. Springer, Berlin (2007)Google Scholar
  63. 63.
    Hosny, M., Mumford, C.: New solution construction heuristics for the multiple vehicle pickup and delivery problem with time windows. In: Proceedings of the Metaheuristic International Conference (2009)Google Scholar
  64. 64.
    Huang, Y.H., Ting, C.K.: Ant colony optimization for the single vehicle pickup and delivery problem with time window. In: Proceedings of the International Conference on Technologies and Applications of Artificial Intelligence (2010)Google Scholar
  65. 65.
    Koning, D.: Using column generation for the pickup and delivery problem with disturbances, Utrecht University (2011)Google Scholar
  66. 66.
    Lu, Q., Dessouky, M.M.: A new insertion-based construction heuristic for solving the pickup and delivery problem with time windows. Eur. J. Oper. Res. 175, 672–687 (2006)CrossRefzbMATHGoogle Scholar
  67. 67.
    Nagata, Y., Kobayashi, S.: A memetic algorithm for the pickup and delivery problem with time windows using selective route exchange crossover. In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, pp. 536–545 (2010)Google Scholar
  68. 68.
    Pankratz, G.: A grouping genetic algorithm for the pickup and delivery problem with time windows. OR Spectr. 27, 21–41 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  69. 69.
    Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40, 455–472 (2006)CrossRefGoogle Scholar
  70. 70.
    Xu, H., et al.: Solving a practical pickup and delivery problem. Transp. Sci. 37, 347–364 (2003)CrossRefGoogle Scholar
  71. 71.
    Pillac, V., et al.: A review of dynamics vehicle routing problems. Eur. J. Oper. Res. 225, 1–11 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  72. 72.
    Kutadinata, R., Thompson, R., Winter, S.: Cost-efficient co-modal ride-sharing scheme through anticipatory dynamic optimisation. In: Submitted to the 23rd World Congress on Intelligent Transport Systems (2016)Google Scholar
  73. 73.
    Bent, R., Van Hentenryck, P.: Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52, 977–987 (2004)CrossRefzbMATHGoogle Scholar
  74. 74.
    Gendreau, M., et al.: Parallel tabu search for real-time vehicle routing and dispatching. Transp. Sci. 33, 381–390 (1999)CrossRefzbMATHGoogle Scholar
  75. 75.
    Gendreau, M., Laporte, G., Séguin, R.: Stochastic vehicle routing. Eur. J. Oper. Res. 88, 3–12 (1996)CrossRefzbMATHGoogle Scholar
  76. 76.
    Yang, W., Mathur, K., Ballou, R.: Stochastic vehicle routing problem with restocking. Transp. Sci. 34, 99–112 (2000)CrossRefzbMATHGoogle Scholar
  77. 77.
    Rigby, M., Winter, S.: Enhancing launch pads for decision-making in intelligent mobility on-demand. J. Location Based Serv. 9(2), 77–92 (2015)CrossRefGoogle Scholar
  78. 78.
    Broll, G., et al.: Tripzoom: an app to improve your mobility behavior. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia (MUM ’12), pp. 57:1–57:4. ACM, New York, NY, USA (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nicole Ronald
    • 1
  • Zahra Navidi
    • 2
  • Yaoli Wang
    • 2
  • Michael Rigby
    • 2
  • Shubham Jain
    • 2
  • Ronny Kutadinata
    • 2
  • Russell Thompson
    • 2
  • Stephan Winter
    • 2
  1. 1.Department of Computer Science and Software EngineeringSwinburne University of TechnologyHawthornAustralia
  2. 2.Department of Infrastructure EngineeringThe University of MelbourneParkvilleAustralia

Personalised recommendations