In-vehicle Time Research Articles

How does the preference heterogeneity affect the elderly's evaluation of bus accessibility? Evidence from Guangzhou, China

IntroductionThe elderly population in China relies heavily on public transit. The ability of the elderly to access public transit services is linked to their social participation. However, public transit does not address many of the needs of the elderly, and research on the accessibility of public transit for the elderly is insufficient, especially in developing countries such as China. The novelty of this study is that it analyzes the elderly's demographic characteristics, latent variables, and heterogeneity in order to evaluate the accessibility of public transit. It also deepens an understanding of the elderly's travel behavior. MethodsThe primary data for this study were drawn from a survey in Guangzhou, China. A confirmatory factor analysis was used to obtain the elderly's latent variables. After that, a latent-class logit model (LCM) was used to analyze the elderly's travel behavior. LCM avoids the shortcomings of the traditional multinomial logit model (MNL), and avoids shortcomings when segmenting travelers with arbitrary demographic characteristics. ResultsUsing the latent-class logit model, the elderly could be classified into two latent classes: the walking-preference subgroup and the walking-adverse subgroup. These two latent classes accounted for 38.3% and 61.7% of the total interviewed subjects, respectively. Taking in-vehicle time as a reference, it was found that 1 min of walking time was equivalent to 0.36 and 2.82 min of in-vehicle time for these two latent classes, respectively. This reflected a significant difference. The study assessed several demographic characteristics: gender, education, pension amount, electric bicycle ownership, bus use frequency, and the elderly's psychological effects (perceived physical functioning and psychological factors towards physical activity) in the latent-class logit model. These factors significantly impacted the classification of the latent class at a 10% confidence level. ConclusionThe elderly rely heavily on public transit in China's cities. As such, urban planners should consider the needs of the elderly, and should consider the findings of this study in developing a more comfortable public transit system.

To Pool or Not to Pool? Understanding opportunities, challenges, and equity considerations to expanding the market for pooling

On-demand mobility services such as bikesharing, scooter sharing, and transportation network companies (TNCs, also known as ridesourcing and ridehailing) are changing the way that people travel by providing dynamic, on-demand mobility that can supplement public transit and personal-vehicle use. Adoption of on-demand mobility has soared across the United States and abroad, driven by the flexibility and affordability that these services offer, particularly in urban areas where population density and land use patterns facilitate a reliable balance of supply and demand. The growth of app-based ridesharing, microtransit, and TNCs presents a unique opportunity to reduce congestion, energy use, and emissions through reduced personal vehicle ownership and increased vehicle occupancy, the latter of which is largely dependent on the decisions of individual travelers to pool or not to pool. This research provides key insights into the policy levers that could be employed to reduce vehicle miles traveled and emissions by incentivizing the use of pooled on-demand ride services and public transit. We employ a general population stated preference survey of four California metropolitan regions (Los Angeles, Sacramento, San Diego, and the San Francisco Bay Area) to examine the opportunities and challenges for drastically expanding the market for pooling, taking into account the nuances in emergent travel behavior and demand sensitivity across on-demand mobility options. Although high-frequency TNC users – those that use TNCs once a week or more - are more likely to consider pooling than less frequent users and reflect more multimodal travel behavior than other travelers, we find that the most captive and price sensitive TNC users are often the most vulnerable. Heavy TNC users – those using TNCs more than three days per week - are disproportionately low income, more likely not to own or lease a car and more likely to use TNCs for essential trip purposes than are less frequent users. Pooling demand sensitivity varies significantly across trip contexts, metropolitan regions, socio-demographics, travel behavior, and attitudes and perceptions toward sharing. We estimate the time and price tradeoffs in choosing between ride alone and shared on-demand service options, finding significant differences across values that travelers place on each component of travel time (wait time, access/egress walking time, and in-vehicle time) by geography and income level. We discuss the potential to leverage these insights to develop policies that combine pricing, curb management, and promotional strategies to increase the pooling market share.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

Designing Multiple Short-Turn Routes to Mitigate the Crowding on a Bus Network

On a transit route, initiating a trip beyond a transit route’s departure terminus and terminating the trip before its arrival is called “short-turn.” This study develops a method to design short-turn services to alleviate crowding on a transit network consisting of multiple overlapping routes. The proposed method consists of two processes: (i) optimizing routes, turn-back points, and fleet sizes of short-turn services by a genetic algorithm to minimize the sum of waiting, in-vehicle, operation, and social costs; (ii) assigning passenger trip demands to short-turn and existing services. These processes are performed iteratively to design multiple short-turn routes. The proposed method is applied to a real-world transit network in Seoul, Republic of Korea, to design 10 short-turn routes. The results are compared with two naïve methods that introduce short-turn services to sections with the maximum occupancy and the maximum demand divided by the section length, respectively. The comparison results show the proposed method is much superior to both of the naïve methods in reducing total costs. The result of the sensitivity analysis suggests that the value of in-vehicle time needs to be estimated accurately and site-specifically. The proposed model contributes to enhancing the convenience of transit users by effectively mitigating the crowding under budget constraints.

Commuting in the Storm: Adaptation of Transit Riders and Measures for Transit Operator—A Case in Shanghai

Adverse weather has been recognized as an important factor to affect travelers’ activity plans in departure time, transport mode, route taken, or cancellation. In the storm, road waterlogging degrades the capacity of road networks and the service quality of transit systems, which further may affect the supply and demand for transit. Based on a typical case in Shanghai that commuters have no easy access to metro service, this paper aims to explore how transit passengers adapt to different situations in the storm and what emergency plans should be taken accordingly. Derived from the revealed preference (RP) and stated preference (SP) survey results for experienced transit commuters, a nested logit (NL) model was developed to describe the travel behavior of transit commuters. Six alternatives, Direct Bus, Bus + Bus, Metro, Bus + Metro, Taxi, and Cancel Trip, and three storm scenarios were set for transit commuters in this case. Estimated parameters indicate that, in storm weather, crowdedness is less considered by transit commuters, and transfer times, walking time, in-vehicle time, and waiting time have negative effects on the selection of the corresponding alternative, whereas the impact of taxi fare is positive since the higher fare is usually accompanied by worse weather and traffic condition. Sensitivity analysis shows that walking time to metro station, in-vehicle time, and waiting time at a bus stop are the most critical factors leading to transit ridership reduction in the storm. According to this, three possible plans for the transit operator, shuttle bus to the metro station (P1), information announcement (P2), and route adjustment (P3), are simulated and compared. We recommend adopting P2, P2 + P3, and P1 + P2 + P3 in turn with the increase of road waterlogging. These findings have important practical implications for developing transit emergency management plans and serve as references for the transit agencies and operators.

Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers

Given the aim of increasing public transport patronage, it is important to understand how passengers perceive different trip characteristics. Most of the existing studies about public transport demand and route choice assigned a higher value of time to transfers than in-vehicle time and used a general transfer penalty to capture an average increase in the travel disutility because of the amount of transfers. However, it is likely that there are nuances to the transfer behaviour depending on specific transfer conditions that existing models do not capture and hence it is difficult to evaluate measures aimed at improving transfers to make public transport more attractive.

Cooperatively coevolutionary optimization design of limited-stop services and operating frequencies for transit networks

The objective of the limited-stop service design and frequency setting problem (LSDFSP) is to realize the operation of a transit route with a set of elaborate service patterns and corresponding frequencies that can minimize the total social cost of the users and operators. In practice, these different patterns and frequencies involve a trade-off between the user and operator requirements, and this aspect cannot be fully clarified by a single-objective optimization problem. Therefore, in this study, the LSDFSP is considered at the network level and formulated as a multi-objective optimization problem with competing objectives of minimizing the user and operator costs. The cooperative coevolutionary multi-objective evolutionary algorithm is redesigned to collaboratively optimize the service patterns and frequencies. A prioritization method is proposed to separately incorporate different types of unsatisfied demand as critical indicators, prompting the algorithm to dig deeper into valuable genes and evolve more feasible solutions. The proposed algorithm is tested on a small network and a real intricate network. It is noted that higher frequencies increased the fleet size and decreased the users’ waiting time and in-vehicle time. Furthermore, skipping more stations reduced the fleet size and users’ in-vehicle time, while increasing the users’ waiting time and number of transfers. The computational results indicated that the proposed algorithm can suitably incorporate the trade-offs and generate an accurate set of Pareto-optimal solutions.

Preferences for shared automated vehicles: A hybrid latent class modeling approach

We model the mode choice between three novel emerging transportation services and identify latent market segments not studied yet in the context of shared automated vehicles, including ridesharing, car sharing and automated transit, using a hybrid choice model. We use a discrete distribution to capture taste heterogeneity of distinct latent classes. Latent variables, socio-demographics and travel habits inform latent class assignment estimated simultaneously with a discrete choice kernel. Respondents chose their preferred mode for going to work in a set of stated preference choice tasks, based on the attributes of their current commutes using a Bayesian D-Efficient design. Users were segmented into two latent classes based on latent factors that capture time style orientation and public transit dislike. The effects of seating designation, not modeled previously in this context, trip cost and travel times in a shared ride, were estimated for the two classes. Users who neither like transit nor ridesharing with strangers are less likely to choose a shared ride if their designated seat is the middle seat, and overall less likely to choose automated transit. Individuals who have more organized time styles demonstrate higher marginal sensitivity to travel times and costs and are more likely to choose automated transit. Value of time analysis reveals that wait time of services that offer a convenient home pickup is valued lower than in-vehicle time. The implications for future adoption of shared automated vehicles is further discussed.

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

In this research, a feasible mechanism is developed to determine the optimum number of bus rapid transit (BRT) stations as well as their respective locations along the service corridor. To accomplish this, a mathematical model is developed and optimized by using three different evolutionary algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), and the results are compared. The total cost function is composed of two main costs namely the operator’s cost, i.e., related to costs on service provider’s end, and the user’s cost, i.e., related to costs on commuters’ end. A functional numerical example with the commuters’ demand is worked out by minimizing the cost function, which demonstrates the applicability of the framework. In our case study, PSO outclassed GA and DE on the basis of convergence rate. Since our work has proved the robustness of PSO as compared to GA and DE, we conducted our sensitivity analysis keeping PSO as our benchmark algorithm to study the influence of various parameters on the optimal cost. The computational experiments reveal that the optimal cost is substantially affected by the variations in the commuters’ demand, commuters’ walking speed, and value of the users’ access and in-vehicle time. On the contrary, the acceleration/deceleration delays at a bus station, bus operating cost, and headway have an inconsiderable impact on the optimal cost.

Quantifying service-reliability-based day-to-day evolution of travel choices in public transit systems with smart transit card data

This study develops day-to-day models to explore how unreliability of public transit service affects the day-to-day evolution of travel choices. We consider two dynamical processes that incorporate transit service unreliability, i.e., travelers' learning and perception updating process (LPUP) and proportional-switch adjustment process (PSAP). The conditions for existence, uniqueness, and stability of the fixed point of each model are analytically derived. These conditions are then examined using real-world public transit data from the Greater Sydney area. We find that with some aggregations, the system stability conditions are satisfied in both models. The observed weighted average flow change between two successive days is around 6.5% over the observation period, which may reflect the system stochasticity rather than instability. Among a series of empirical findings, it is noteworthy that in the Sydney case, the value of service schedule delay is around 3.27 times that of in-vehicle time.

Evaluating accessibility of small communities via public transit

Accessibility to and from urban centres allows small communities’ dwellers to participate in primary activities and use essential services that are not available on-site, such as educational, work and medical services. Public transport networks are supposed to enhance accessibility and pursue equity principles, overcoming socio-economical differences among people that can exacerbate during crisis. In this paper a methodology is proposed and implemented to assess small communities’ accessibility via public transit. A metric is defined based on the calculation of total travel time, taken as a proxy of travel impedance, with consideration of in-vehicle time, schedule delay and users’ arrival and departure preference curves (i.e. time-of-day functions). A “rooftops” model is specified and implemented under the assumption that travellers cannot accept (scheduled) late arrival or early departure time penalties before and after the participation in their activities in the main urban centre, as many activities rarely admit time-flexibility. Also, a public transport specific impedance factor (PTSIF) is proposed, in order to account for travel impedance determinants, which are a consequence of service scheduling and routing decisions and not due to inherent geographical and infrastructural disadvantages affecting car users too. An application of the methodology for the city of Cesena, Italy, and 90 surrounding small communities is presented. The city is served by train and bus services. Assessment of small communities' accessibility based on both total travel time and PTSIF is presented and discussed. This practice-ready quantitative method can help transport professionals to evaluate impacts on small communities’ accessibility in light of public transport service changes or reduction. Quantitative approach to support strategic decisions is needed, for example, both to assess public transport strengthening politics against depopulation of rural and marginal mountainous areas and to mitigate the effects of possible increasing concentration of services towards high-demand lines, which may follow as a consequence of budget cuts or contingencies, such as vehicle capacity reductions required by sanitary emergencies.

Public transport accessibility accounting for level of service and competition for urban opportunities: An equity analysis for education in Santiago de Chile

Several cities around the world have changed their transportation planning paradigm, understanding that the prime goal is to provide access to opportunities for everyone. To address this goal, public transport plays a fundamental role and, therefore, it is key for developing a sustainable and equitable city.This paper proposes a methodology to analyze access to opportunities through public transport incorporating the user's valuation of attributes that impact the level of service on his/her trip and the competitiveness for urban opportunities. Using data from Santiago, Chile, we applied the proposed methodology to analyze accessibility to higher-quality public primary schools. We compare total travel time (TTT) with a proposed measure of total generalized travel time (TGTT) using simple potential and competitive accessibility indicators, accounting for the subjective valuation of walking time, travel time, waiting time, comfort and transfers, and translating them into in-vehicle time units.We find that the inclusion of competition has a more substantial impact than including the subjective valuation of the level of service in the accessibility to educational opportunities. Using competitive measures with TGTT, we found that around 20% of the zones in Santiago have at least a 50% deficit of higher-quality public education, and 71% of them are in peripheral areas. Furthermore, these zones, where medium and low-income population usually lives, can experience, on average, 1–2 transfers, 4–5 passengers per square meter, and 15-min waiting. We conclude that the proposed methodology provides a more comprehensive way to understand accessibility by incorporating the traveling experience, allowing to determine how and where to intervene to effectively improve accessibility, with a focus on urban equity.

Integrated timetable rescheduling and passenger reassignment during railway disruptions

During railway disruptions, most passengers may not be able to find preferred alternative train services due to the current way of handling disruptions that does not take passenger responses into account. To offer better alternatives to passengers, this paper proposes a novel passenger-oriented timetable rescheduling model, which integrates timetable rescheduling and passenger reassignment into a Mixed Integer Linear Programming model with the objective of minimizing generalized travel times: in-vehicle times, waiting times at origin/transfer stations and the number of transfers. The model applies the dispatching measures of re-timing, re-ordering, cancelling, flexible stopping and flexible short-turning trains, handles rolling stock circulations at both short-turning and terminal stations of trains, and takes station capacity into account. To solve the model efficiently, an Adapted Fix-and-Optimize (AFaO) algorithm is developed. Numerical experiments were carried out to a part of the Dutch railways. The results show that the proposed passenger-oriented timetable rescheduling model is able to shorten generalized travel times significantly compared to an operator-oriented timetable rescheduling model that does not consider passenger responses. By allowing only 10 min more train delay than an optimal operator-oriented rescheduling solution, the passenger-oriented model is able to shorten the generalized travel times over all passengers by thousands of minutes in all considered disruption scenarios. With a passenger-oriented rescheduled timetable, more passengers continue their train travels after a disruption started, compared to a rescheduled timetable from the operator-oriented model. The AFaO algorithm obtains high-quality solutions to the passenger-oriented model in up to 300 s.

Assessing operation and customer perception characteristics of high frequency local and limited-stop bus service in Vancouver, Canada

Public transport agencies implement different strategies aimed at improving the operation of transit service and to increase satisfaction among its riders. One service strategy employed by transit agencies is a limited-stop bus service that runs in parallel to a heavily used route to decrease travel times for existing riders and to reduce pressure on the local route. Using bus operations data obtained from automatic vehicle location (AVL) and automatic passenger counter (APC) systems and customer satisfaction data collected in Vancouver, Canada, the present study evaluates levels of satisfaction among users of a local and limited-stop bus service while controlling for the service characteristics these users have experienced in the past seven days. Our results reveal that after controlling for characteristics related to the conditions of the service experienced by users, namely passenger activity levels, patrons of the express route service were more likely to be satisfied with the transit service compared to users of the local service. This finding indicates that the operational characteristics of a limited-stop service, including in-vehicle time savings and higher route frequency, are highly valued by its users. Results of this study demonstrate how operations data can provide a greater context for customer satisfaction analyses. Finally, this study provides transit planners and policy makers with a better understanding of how customers perceive local and limited-stop service.

Exploring the Relationship between Transport Interventions, Mode Choice, and Travel Perception: An Empirical Study in Beijing, China.

Transport interventions help to facilitate the sustainable travel behavior. The effects of transport interventions on travel choices have been addressed extensively. However, little research has been devoted to the influence of transport interventions and travel choice on travel perception. This study aimed to investigate the relationship among the three aspects. Two intervention measures, information intervention and public transport service improvement, were selected. Intervention experiments were designed to collect mode choice and corresponding travel perception in different experiment stages. Process models of information intervention and public transport service improvement were proposed. The results show that information intervention only had a minor effect on mode choice and had no direct effect on travel perception. Public transport service improvement in in-vehicle time and comfort enhanced public transport use dramatically. Comfort improvement also had positive effects on travel perception. Walking had positive and public transport trips had negative effects on travel perception. For travelers who had a high evaluation of car trips, the probability of green mode use would decrease. Travelers who gave high marks to trips by green mode would have a higher probability to keep traveling by green mode. This study contributes to facilitating public transport use and enhancing positive perception during traveling.

The potential impacts of automated cars on urban transport: An exploratory analysis

ObjectiveThe concept of automated cars is rapidly becoming a reality. Yet there has been very little analysis of the impacts of such developments on the performance of urban transport systems. These impacts are potentially complex. On the positive side, automation has the potential to increase road capacity, make driving available to more people, and reduce accidents and emissions. On the negative side, it could attract users away from public transport, walking and cycling, substantially increase traffic levels and stimulate urban sprawl. These impacts cannot currently be measured empirically and, by the time that they can, it will be too late to change the implementation model to rectify any resulting problems. Predictive assessments are therefore needed. This paper considers the possible impacts of automated vehicles, predicts their effects on the urban land use and transport system, and discusses the policy implications. We focus on automation of the car fleet, and do not consider the potential of automation of public transport or freight. MethodsWe consider the literature on the range of attributes of automated vehicles which might affect transport and land use patterns, and suggest potential outcomes for each over the period to 2050. These include the proportion of automated vehicles in the car fleet, whether automated vehicles are privately purchased or publicly shared, the impacts on network capacity, the reduced need to pay for and walk from parking places, the potential reduction in the value of in-vehicle time and the potential use by current non-drivers. We represent these attributes in an expanded causal link diagram of the urban land use and transport system and import those causal links into the MARS system dynamics model. We determine from the literature a level for each attribute, and test the impacts in a set of ten scenarios using an updated MARS model of Leeds. ResultsBased on our input assumptions, we find that car-km in 2050 could be over 50% higher than in the business as usual scenario. Public transport use could fall by 18%, threatening accessibility for those dependent on it, while walking and cycling could fall by 13%, reducing their health benefits. Overall person-km would rise, suggesting a reduction in sustainability. A requirement that all automated cars are shared vehicles could reduce these adverse impacts somewhat, but the effects are sensitive to the charge per km. DiscussionOur use of a single value for each attribute means that our analysis is exploratory, but the size of the resulting impacts demonstrates the importance of understanding the scale of systems response to each of the attributes which we have considered. It will be important to manage the way in which automated cars are introduced into urban areas, if they are not to lead to a worsening of the urban environment, accessibility and health. A requirement to make all such vehicles part of shared fleets offers one way forward, but more work is needed to understand the way in which use of such fleets should be charged.

What are the determinants of the willingness to share rides in pooled on-demand services?

Simulation studies suggest that pooled on-demand services (also referred to as Demand Responsive Transport, ridesharing, shared ride-hailing or shared ridesourcing services) have the potential to bring large benefits to urban areas while inducing limited time losses for their users. However, in reality, the large majority of users request individual rides (and not pooled rides) in existing on-demand services, leading to increases in motorised vehicle miles travelled. In this study, we investigate to what extent fare discounts, additional travel time, and the (un)willingness to share the ride with (different numbers of) other passengers play a role in the decision of individuals to share rides. To this end, we design a stated preference study targeting Dutch urban individuals. In our research, we (1) disentangle the sharing aspect from related time–cost trade-offs (e.g. detours), (2) investigate preference heterogeneity regarding the studied attributes and identify distinct market segments, and (3) simulate scenarios to understand the impact of the obtained parameters in the breakdown between individual and pooled services. We find that less than one third of respondents have strong preferences against sharing their rides. Also, we find that different market segments vary not only in their values of the willingness to share, but also in how they perceive this willingness to share (per-ride or proportional to the in-vehicle time). Further, the scenario analysis demonstrates that the share of individuals who are willing to share rides depends primarily on the time–cost trade-offs, rather than on the disutility stemming from pooling rides per se.

A cuckoo search based approach to design sustainable transit network

ABSTRACT Transit Network Design and Frequency Setting Problem (TNDFSP) is considered as a multi-objective optimization problem in which the routes of the transit network are determined while the operational constraints are taken into consideration. In this study, an optimization approach based on cuckoo search (CS) algorithm was proposed to solve this problem. Apart from the development of the optimization algorithm, the model incorporated sustainable design objectives and minimized the weighted sum of operator, user and external costs. Operator cost included the number of buses required to operate in the network and the facilities required to operate electric buses. The user costs consisted of waiting and in-vehicle travel time along with the number of transfers. Moreover, the pollutants emitted by conventional buses were considered as the external (environmental) cost. The results were compared on the basis of Mandl’s network. . Overall, the CS-based approach performed significantly in finding the optimal routes for the public transportation network.

Dynamic carpool in morning commute: Role of high-occupancy-vehicle (HOV) and high-occupancy-toll (HOT) lanes

This study analyzes carpool in morning commute, using a bottleneck model that allocates a fraction of the bottleneck capacity to a high-occupancy-vehicle (HOV) or a high-occupancy-toll (HOT) lane. Commuters are homogeneous cost minimizers who participate carpool through a platform, either as solo driver, carpool driver or carpool rider. At user equilibrium (UE), no commuter can unilaterally lower her cost by switching to a different departure time, carpool role or road facility. Because a rider can use the in-vehicle time more productively than her driver, she enjoys a cost benefit that grows with the journey time. Therefore, the driver must be compensated more when travel delay due to congestion is longer. This requirement is fulfilled through a time-dependent carpool ratio that determines how much a driver is paid. We characterize and numerically obtain the UE solutions for this dynamic carpool problem. Results of numerical experiments show that the HOV lane promotes carpool and boosts welfare. The larger capacity the HOV lane is allocated, the greater benefit it offers to the system. In addition, an HOT lane can bring additional welfare gains with a modest level of toll.

Beyond the Mohring effect: Scale economies induced by transit lines structures design

In this paper we study how the spatial arrangement of transit lines (lines structure) influences scale economies in public transport. First we show that the degree of scale economies (DSE) increases discretely whenever passenger volume induces a change in lines structure. The technical elements behind this are explained by using a new three-dimensional concept called directness, encompassing number of transfers, number of stops and passenger route lengths. This is first exemplified in a simple ad-hoc network, and then applied to examine the structural changes that occur in the design of transit lines in a fairly general representation of a city. We show that directness increases whenever lines structure changes as a response to larger demand volumes - increasing DSE at the particular value of flow where this change occurs - because systems with more direct lines for each OD pair diminish in-vehicle times while increasing waiting times mildly, such that users are benefited by lower travel times and operators are benefited by lower idle capacity. After the change, however, DSE decreases within the demand range where the new line structure is maintained, just as in the one line model. The possibility of deciding the line structure introduces directness as a new source of economies of scale which are finally exhausted after full directness is achieved.