Potential Travel Demand Research Articles

On the relationship between air connectivity and economic development: A comparative analysis of inequality evolution for 2000–2019

Throughout the past decades, aviation has experienced a tremendous growth of passenger and cargo demand. This growth may come with problems however, when considering the Sustainable Development Goals set by the United Nations in 2015, particularly those related to the sustainable and fair use of resources and reduction of inequalities among countries. This study discusses the air connectivity inequalities of countries from various perspectives, covering topology-based and schedule-based connectivity indicators. We focus on the connectivity changes of countries between the years 2000 and 2019, i.e., before the impact of the COVID-19 pandemic. Results indicate that our aviation system is dominated by a few highly-developed countries in Western Europe and Northern America. While this dominance is gradually weakened, mainly due to the emergence of aviation hubs in the Middle East and the role of China as a hub in Asia, there need to be more efforts towards a homogeneous aviation infrastructure. Moreover, the flight connectivity between countries appears to be correlated to the Human Development Index growth value, but not significantly correlated with the potential travel demand between countries. Finally, detailed analysis on network-induced inequalities of connectivity reveal that countries are most unequally distributed concerning betweenness centrality but most equally according to closeness centrality. We believe that our study contributes towards a better understanding of sustainable aviation planning and raises important policy issues concerning the achievement of Sustainable Development Goals.

A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency

Unconventional events exacerbate the imbalance between regional transportation demand and limited road network resources. Scientific and efficient path planning serves as the foundation for rapidly restoring equilibrium to the road network. In real large-scale road networks, especially during emergencies, it is usually difficult to obtain or predict accurate dynamic traffic network flows in real-time, which is used to support equilibrium path planning. Moreover, the traditional iterative methods cannot meet the real-time demand of emergency equilibrium path planning decision generation. To solve the above problems, this paper proposes a hybrid control architecture for path planning based on equilibrium traffic assignment theory. The architecture introduces the travelers’ real-time travel data and constructs a spatio-temporal neural network, which captures the evolution of traffic network loads. Adaptive multi-graph fusion technology is used to mix the background traffic flow data and the traveler’s real-time Origin–Destination (OD) data, to mine the dynamic correlation between the traffic state and the travelers’ travel. Based on the real-time prediction of dynamic network states, equilibrium mapping learning is carried out to pre-allocate potential travel demands and construct equilibrium traffic graphs based on system optimization traffic assignment. Finally, individual evacuation path strategies are generated online in a data-driven manner in real time to achieve improved resilience in the transportation system.

Preliminary study of nation-wide urban air mobility demand and vertiport network study in Germany

This study proposes a vertiport network for eVTOL in Germany. We use empirical travel survey data to estimate demand and calculate service "supply" based on the available space. Through a sensitivity study for different mode shares the resulting space requirements are determined. Both the potential travel demand and the potential supply of aircraft movements on the ground are not well known. Connections are evaluated regarding the possible travel time savings and the value of time of the travelers on the corresponding route. The empirical data used for this study was collected and is provided by the Federal Ministry of Transport and Digital Infrastructure Germany. The MiD study is conducted at regular intervals and provides “extensive key values for the year 2017 on day-to-day mobility among Germany's residential population and refers to all trips they made within Germany” (The Federal Ministry of Transport and Digital Infrastructure, 2017).

A Model of Passenger Demand on the Operation of LRT at Ngurah Rai Airport Corridor in Bali

In the planned of LRT procurement for the Kuta - Ngurah Rai Airport route, it is necessary to determine the potential travel demand and passenger characteristics, so that the government and the industry involved can plan their infrastructure and facilities needs appropriately. The results analysis show that passengers are included in the choice group, meaning that almost all have private vehicles but they have high motivation for the availability of safe, comfortable and on time public transportation. Potential Demand Analysis with Stated Preference shows the highest choice is LRT/ART of Airport-Kuta-Sanur route 66.50% of passengers, the second choice using private vehicles 22.10% of passengers. The potential demand model for LRT/ART users can be well explained by the individual passengers characteristics with a significance level of 84.807%, and the effect of all variables on the model is 54.3%. The characteristics of passenger trips can also explain the model with a significance level of 87.342%, and the effect of all variables on the model is 65.8%. With this model, it is hoped that the LRT / ART type mass transit system will be able to compete with private cars, taxis or online-based public transportation.

Identifying the Service Areas and Travel Demand of the Commuter Customized Bus Based on Mobile Phone Signaling Data

In recent years, customized bus (CB), as a complementary form of urban public transport, can reduce residents’ travel costs, alleviate urban traffic congestion, reduce vehicle exhaust emissions, and contribute to the sustainable development of society. At present, customized bus travel demand information collection method is passive. There exist disadvantages such as the amount of information obtained is less, the access method is relatively single, and more potential travel demands cannot be met. This study aims to combine mobile phone signaling data, point of interest (POI) data, and secondary property price data to propose a method for identifying the service areas of commuter CB and travel demand. Firstly, mobile phone signaling data is preprocessed to identify the commuter’s location of employment and residence. Based on this, the time-space potential model for commuter CB is proposed. Secondly, objective factors affecting commuters’ choice to take commuter CB are used as model input variables. Logistic regression models are applied to estimate the probability of the grids being used as commuter CB service areas and the probability of the existence of potential travel demand in the grids and, further, to dig into the time-space distribution characteristics of people with potential demand for CB travel and analyze the distribution of high hotspot service areas. Finally, the analysis is carried out with practical cases and three lines are used as examples. The results show that the operating companies are profitable without government subsidies, which confirms the effectiveness of the method proposed in this paper in practical applications.

Needs-gap analysis of street space allocation

Streets have long been designed to maximize motor vehicle throughput, ignoring other street users. Many cities are now reversing this trend and implementing policies to design more equitable streets. However, few existing tools and metrics enable widescale assessment, evaluation, and longitudinal tracking of these street space rebalancing efforts, i.e., assessing how equitable the current street design is, how it can be improved, and how much progress has been made. This paper develops a needs-gap methodology for assessing the discrepancy between transportation supply and demand in urban streets using existing datasets and automated methods. The share of street space allocated to different street users is measured in 11 boroughs of Montréal, Canada. Travel survey data is used to estimate the observed and potential travel demand in each borough in the AM peak period. A needs-gap analysis is then carried out. It is found that bus riders and cyclists face the greatest needs-gap across the study area, especially in central boroughs. The needs-gap also increases if only trips produced or attracted by a borough are considered. This shows the potential of applying an equity-based framework to the automated assessment of street space allocation in cities using large datasets.

Integrating Price-Incentive and Trip-Selection Policies to Rebalance Shared Electric Vehicles

Because electric vehicle sharing (EVS) offers the advantages of high flexibility and convenience, it has been receiving increasing attention worldwide as an effective approach to easing traffic congestion and environmental pollution. However, unbalanced electric vehicle distribution is an obstacle in the development of EVS. In this paper, we propose an integrated strategy to mitigate the imbalance issue and enhance customers’ adoption of EVS. We construct an integrated strategy that combines the price-incentive approach with the trip-selection policy and models uncertain travel demand in a continuous trip-adopting process based on our integrated strategy. Aiming to improve EVS operating profits, we apply spatiotemporal nonlinear mixed-integer programming to formulate the travel pricing and rebalancing plan. Additionally, we approximate the model in a tractable form after analyzing the optimal service adoption and develop an efficient exact algorithm to handle the nonlinear items. The computational results of a real-world car2go Amsterdam case study demonstrate several economic and environmental benefits generated by our integrated policy, including (i) higher profits for EVS operators, (ii) improved service satisfaction for consumers, and (iii) a higher level of carbon emissions reduction, from 381 grams per mile to 225 grams per mile, beneficial for the social environment. Moreover, according to the case study, an appropriate initial fleet size, high rebalancing frequency, low labor cost, high potential travel demands, and short charging time also benefit EVS operation.

Intermodal Connection of High-Speed Rail with Interregional Bus Services in Japan

An intermodal transportation service consisting of high-speed rail (HSR) and an interregional bus service is one policy option for rural areas where interregional travel demand is too low to justify the construction of HSR. This study reviews current interregional bus services connecting with interregional rail, particularly HSR, in Japan, and analyzes the market potential for improving intermodal transportation by integrating HSR with an interregional bus service. It reviews the current interregional transportation network and related travel demand, including for air, rail, and bus. It also analyzes the connectivity of rail+bus intermodal transportation. The analysis showed poor connectivity of HSR and interregional bus services in Japan. Next, an interregional travel mode choice model is estimated with a nested-logit model using data from the Interregional Travel Survey 2010. Then, origin–destination pairs constituting the potential travel demand of the rail+bus option are identified using simple market analysis. This revealed that origin–destination pairs connecting prefectural cities along the Tohoku Shinkansen (HSR) with Kofu City could gain modal shift from other travel modes to rail+bus through improvement in the connection or introduction of a new interregional bus service connected with HSR. Expected changes in modal shares for rail+bus are estimated through a case study where a connection at the HSR station is hypothetically improved by a newly introduced interregional bus service. This suggests that improvements in connectivity at the HSR station could encourage the intermodal transportation service of rail+bus, even for areas not connected with the HSR network.

A Cooperative Data Mining Approach for Potential Urban Rail Transit Demand Using Probe Vehicle Trajectories

Promoting the use of public transportation is an important approach to develop sustainable mobility. However, lots of potential users of public transportation chose taxi, a semi-private mode for convenience. In this study, we first define this potential urban rail transit demand based on its spatiotemporal features. Then a novel data mining method is proposed to ascertain the potential urban rail transit demand from taxi trajectory data through considering spatial and temporal constraints simultaneously. Two features of the potential demand, i.e., the zero rates and volatility, are obtained by the combination of statistical and feature extraction (local neighbor descriptive pattern, LNDP) techniques. They are used to classify the urban rail transit stations into different categories which need different improvement measures to promote the attraction to the potential users. The effectiveness of the proposed method is tested using the GPS trajectory data of Shanghai collected from over 10,000 taxis in 12 consecutive days. We find that most urban rail transit stations have the potential to absorb the regular part of taxi ridership. Moreover, obvious imbalances exist between access and egress potential travel demands at these stations. The results show that metro stations can be classified into six groups according to the time-varying laws of potential travel demand, four of which need urgent measures. These findings provide useful insights for developing more effective and targeted strategies to encourage travelers to shift to public transportation. The estimated method of potential demand is the prerequisite for further optimization models.

CB-Planner: A bus line planning framework for customized bus systems

A customized bus (CB) system is an emerging public transportation that aims to provide direct and efficient transit services for groups of commuters with similar travel demands. Existing CB systems aggregate similar travel demands and plan bus lines manually, which is inefficient and costly. In this paper, we propose a CB line planning framework called CB-Planner, which is applicable to multiple travel data sources. A mathematical programming formulation is proposed to simultaneously optimize bus stop locations, bus routes, timetables and passengers’ probabilities of choosing CB. We then developed a heuristic solution framework that includes a grid-density based clustering method for discovering potential travel demands efficiently, a bus stop deployment algorithm to minimize the number of stops and walking distance, and dynamic programming based routing and timetabling algorithms for maximizing estimated profit. We conduct an experiment on a small-scale network to verify the performance gap between the optimal solution and our proposed heuristic solution. A case study is then conducted on one-month taxi trajectory data in Nanjing, China. The study demonstrates that CB lines generated by our CB-Planner can achieve higher profit compared with baseline methods, and they also provide efficient transit services with short walk distances and small departure time adjustments. The moderate increase in travel time is paid off by the significant savings in travel fare.

Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu

Car-sharing is becoming an increasingly popular travel mode in China and many companies invest plenty of money on that including vehicle enterprises and Internet companies. But most of them build car-sharing stations by their experience or randomly as long as there is parking space in the early development of their business. This results in many stations with low operational efficiency and causes capital loss. This study aims to use different data source with statistical models and machine learning algorithm to help car-sharing operator to choose the optimal location of new stations and adjust the location of existing stations. We select Chengdu where there are huge amounts of car-sharing travel demand and several large car-sharing operators as the research area and two main operators as the research objects. Chengdu is divided into 58724 squared grids each of which is 0.5km⁎0.5km instead of focusing on the buffers generated by stations. We try to find a model to estimate a potential travel demand value for each small grid with three data sources: order data, population data, and Point of Interest (POI) data. This problem is transformed into a binary form and five different methods, Logistic Regression, Logistic Regression with LASSO, Naive Bayes, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, are implemented. The optimal model, Logistic Regression with LASSO, is chosen to estimate the probability of existence of demand in all grids. With car-sharing order data from different operators, an existing order heat value is also computed for each grid. Then we analyze and classify all the grids into four groups. For different groups of grids, we give different suggestions on the optimal location of stations. This study focuses on a more competitive market and finds the influential factors on order number. Suggestions on the optimal location of stations are given in consideration of competitors. We hope that our research can help operators improve their business and make rational plans.

Investigating Intercity Rail Transit Scope with Social Economy Accessibility: Case Study of the Pearl River Delta Region in China

In response to challenges caused by high gasoline prices, traffic congestion, and greenhouse gas emissions, smart internment on intercity rail transit infrastructures and service suggests a rekindling of many countries’ interest in offering a range of benefits over automobile travels. In order to check the suitability of the proposed intercity rail transit system with the local conditions in a region, models for relating the intercity rail transit scope and social economy factors have been developed with the datasets obtained in the Pearl River Delta (PRD) region in Guangdong Province, China. In this paper, the accessibility-based approach is presented to explore the impact of the intercity rail transit system planning on the regional development. The impact of three typical accessibility variables, transportation, population, and economic accessibilities, are considered in the approach. The global rail transit scope planning models are developed by using the regression technique to correlate the lengths of 254 regional intercity rail transit systems in different countries in the world with social economy factors. Those models are used for estimating the intercity rail transit size for the PRD region. The modeled transit scopes are examined with spatial distributions of the defined three accessibilities at each node (or centroid center of a town) in a Geographic Information System environment. The developed method has been proven helpful to understanding the gap between transport supply and potential travel demand and the suitability of each node to alignment of each rail transit route through the PRD region case study.

Siting of Carsharing Stations Based on Spatial Multi-Criteria Evaluation: A Case Study of Shanghai EVCARD

Carsharing is one of the effective ways to relieve the problems of traffic jams, parking difficulties, and air pollution. In recent years, the numbers of carsharing services and their members have remarkably increased around the world. The project of electric carsharing in Shanghai, called EVCARD, has also developed rapidly with very large demand and supply. Aiming to determine the optimal locations of future stations of the EVCARD, this research employs a novel method combining the analytic hierarchy process (AHP) and geographical information system (GIS) with big data. Potential users, potential travel demand, potential travel purposes, and distance from existing stations are selected as the decision criteria. A siting decision system is established, consisting of 15 evaluation indicators which are calculated from multi-source data on mobile phones, taxi trajectory, point of interests (POI), and the EVCARD operation. The method of the AHP is used to determine the indicator weights, and the “Spatial Analyst” tool of ArcGIS is adopted to generate the indicator values for every 1 km × 1 km decision unit. Finally, synthetic scores are calculated to evaluate the candidate sites of EVCARD stations. The results of the case study verify the effectiveness of the proposed method, which can provide a more scientific and feasible method for carsharing operators to site stations, avoiding aimless and random decisions.

The Integration of Air and Rail Technologies: Shanghai's Hongqiao Integrated Transport Hub

This paper examines the different approaches to integrating air and rail technologies and their services at the Shanghai Hongqiao Integrated Transport Hub (“the Hongqiao Hub”) in China. Even though the Hongqiao Hub has the advantage of having both an airport and a railway station located at the same place, its level of air-rail integration remains at a low or moderate level. By analyzing existing air and rail services and potential travel demands, this paper recommends further streamlining the relationships among the Shanghai Airport Authority, the Shanghai Railway Bureau, and airline companies by fostering air-rail integration through the improvement of joint ticketing and code-sharing, direct luggage transfer, the provision of the fast and convenient connection from the Hongqiao International Airport to the Pudong International Airport, and air-rail linkage service area expansion.

Analysis of PDR Passenger-Sharing Rate Based on Service Attributes

With safety, comfort, convenience, fast, economy, punctuality advantages and high service effectiveness, the introduction of passenger-dedicated line (PDL) will attract passenger volume assumed by other modes of long-short distance passenger transportation in terminal transportation system as part of its traffic component, named the transferred volume. Meanwhile, PDL will stimulate the potential travel demand to generate induced passenger volume. The volume of terminal passenger flow is of great significance in building PDLs, passenger railway stations, and transportation terminals. From the perspective of transportation mode service, by employing passenger-sharing rate model, Xi'an terminal is presented to estimate and analyze the long and short distance passenger transferred volume and the induced volume which it undertakes after introducing PDL.

Discrete choice models of traveler participation in differential time of day pricing programs

Tolls that vary based on time of day or congestion are gaining attention around the world as a potential travel demand management strategy that can shift peak period travel to off peak periods thereby contributing to peak period congestion relief. However, despite the widespread interest in the concept, there is very little empirical data available on the impacts of variable tolls on traveler choices and disaggregate models that can be used to predict traveler response to variable pricing are few. This paper reports on results from two bridges with differential time of day tolls in the Lee County area of Florida in the United States. Using travel survey data collected at these two bridges, discrete choice models of traveler response to the variable toll rates are estimated. The models indicate that travelers who are retired, have a low income, have flextime at their place of employment, or have a flexible travel schedule are more likely to alter their time of travel with greater frequency due to the variable toll.

Evaluation Study of Special Bus System Based on Comparisons between Actual and Potential Travel Demands of Physically Handicapped People

The special bus system for handicapped, disabled and elderly people in many cities and provincial regions has become a consequential and immediate subject for discussion in transportation planning. The first purpose of this paper is to makeclear the relations between actual and potential travel demands of physically handicapped people. The second is to analyse the major composed factors of the both demands. The third is to evaluate the special bus system in the angle of physically handicapped people. Based on the survey conducted at Isesaki City, Gunma Prefecture, and the investigation using the data, we showed the serviceability ofthe special bus system, and assigned the system in total public transportation system.