Abstract

     Based on the scores and trends of Sustainable Development Goals, Iran, our case study, has been unsuccessful in nearly all the pillars of sustainable development despite the variations in socioeconomic variables related to the SDGs. This implies a comprehensive underdevelopment in Iran, necessitating the improvement of infrastructure which significantly affects all the pillars of sustainable development including environment, health, and economy. This study, takes transportation as an important infrastructure which has various direct and indirect impacts on all pillars of sustainable development, especially in the case study of Iran. To evaluate this, the present study aims to investigate 1) which sustainability perspective dominates in Iran (strong sustainability, weak sustainability, or health-centered sustainability); and 2) the consistency degree of each transportation mode with each pillar of sustainability including environment, health, and economy. We employ VAR and simultaneous equations system to find which perspective of sustainable development dominates Iran; and to rank the transportations modes in accordance with their consistency with sustainable development in Iran during 1979–2016. Overall, the results of this study is found to be significant and support the strong sustainability perspective in Iran. The most compliant transportation modes with the sustainable development in Iran are then ranked as: a) rail, b) air, c) road, and d) maritime transportation. These results suggest the policy-makers to: 1) value the environmental issues at the top of the agenda for the sustainability strategies; and 2) to shift the load of passenger and goods to the rail transportation by investing and developing the rail network.

Introduction

     Starting from the industrial revolution, the vast increase in the production of consumer goods, as well as urbanization has been taking place all around the world. Although such development has had a positive impact on the livelihood of societies, its social reliance and impact on natural resources has increased considerable concerns on this transition. According to the latest data published by the World Bank, the world’s total population has already passed 7.76 billion, and is expected to reach over 9.7 billion by 2050 (World Bank, 2022). Such large population require an ever-increasing dependence on natural resources and the environment [70]. Having foreseen such sustained growth in population, the United Nations (UN) has been one of first intergovernmental entities to organize a series of comprehensive actions over the last few decades to incorporate sustainable means in this socio-technological development. Agenda 21, for instance, has been firstly introduced by the UN in 1992, as a comprehensive plan of actions to enhance the human lives and conserve the environment at the Earth summit in Rio de Janeiro. Later in 2015, the UN published a set of 17 goals, commonly referred to as Sustainable Development Goals (SDGs or also agenda 2030) which outline the objectives necessary to end poverty, inequality, environmental degradation, as well as advocating peace and justice [46,66]. Since then, the doctrine of global sustainability has advanced and become a key element of international sustainability studies and developmental agendas [13,56]. Following this, numerous studies have used the 17 set of goals as a playground to incorporate sustainability in various fields and industries.

Conclusion

     Based on the score of Sustainable Development Goals (SDGs), Iran has been unsuccessful in almost all of the sustainable development goals and pillars. This implies a comprehensive underdevelopment in Iran in various dimensions including environment, social, and economic. To evaluate this, the present study focuses on transportation, since it plays a key role in each single sustainable development pillar in Iran.

     This research tries to find the most consistent transportation mode with the sustainable development pillars in Iran. To find a more accurate ranking of transportation modes, we investigate which perspective of sustainable development dominates in Iran by producing and using HEY model with VAR approach based on World Bank data during 1979–2016. The findings point that the strong sustainability perspective is most effective in case of Iran which implies that the environment plays the most important role in sustainable development, before the social and economic pillars.

Abstract

     While electrification of road transport is a key component of decarbonisation, the implications for the broader economy and related jobs remain underexplored. We quantify these impacts in the EU in a global Computable General Equilibrium (CGE) model, combining techno-economic assumptions about electric vehicles with deployment scenarios derived by energy models. We augment input-output tables underlying the JRC-GEM-E3 model with an explicit representation of vehicle manufacturing and upgrade the modelling of vehicle purchase and operation. Our findings illustrate that greater road transport electrification reduces the overall costs of climate mitigation, primarily driven by lower fuel costs for electric vehicles and a faster decline of battery costs. Transport electrification alters supply-chains and leads to structural shifts in employment from traditional vehicle manufacturing towards battery production, electricity supply and related investments. Finally, we expand the set of labour market indicators to cover skills and occupations, to refine the socio-economic assessments of climate policy.

Introduction

     Global CO2 emissions from transport have continued to rise in recent years across all modes (IEA, 2020a). In the EU, transport greenhouse gas (GHG) emissions were 33 % higher in 2019 compared to 1990 levels, while other sectors achieved emissions reductions over the same period (e.g. energy supply −39 %, industry −35 %, EEA, 2020). This is in stark contrast with the EU’s objectives of 55 % emission reductions by 2030 compared to 1990 and to achieve net zero emissions by 2050.1

     Road transport, accounting for nearly one-fifth of total EU GHG emissions, has therefore become a priority sector for climate action and a key element of the European Green Deal (European Commission, 2019). To encourage rapid emissions reductions in road transport, Europe has recently proposed new initiatives for stronger emissions standards for cars and vans, the set-up of a new emission trading scheme for buildings and road transport, as well as new regulation for alternative fuel and charging infrastructure (European Commission, 2021, European Commission, 2021).

Conclusions and policy implications

     The electrification of road transport is accelerating across the globe, with significant increases in electric vehicle market shares in China, Europe and the United States in particular (IEA, 2020c). While long-term projections on the number of EVs in the car and truck fleets vary across models and methodologies, the technology is widely expected to make up the majority of the total vehicle stock by mid-century, as a result of increasingly stringent climate policies and technology advancements, notably in batteries. Such fleet transformation in the coming three decades will be associated with large shifts in the manufacturing of motor vehicles and their supply-chain in the near-term, as well as changes in the operation of vehicles (fuel shifting, energy efficiency, maintenance schedules, etc.).

     This paper explores the macroeconomic implications of road transport electrification as a contributing policy to reaching Europe’s climate targets. We combine techno-economic assumptions about the composition and operation of the fleet with fuel consumption projections from energy system models into a set of climate mitigation scenarios with varying degrees of road transport electrification. We run these scenarios in an augmented version of the JRC-GEM-E3 model, which explicitly represents the manufacturing of BEVs as a separate economic activity from the manufacturing of conventional ICE vehicles. This new methodology allows for a more detailed analysis of macroeconomic impacts in two major ways: first the supply chain implications of the deployment of EVs can be more clearly identified than if all vehicles were produced using a single input structure. This has large implications in terms of employment, and therefore on occupations and skills. Second, this also allows for a new decomposition analysis, where the various channels of impacts from EVs on macroeconomic results can be explored. Our modelling suggests that a higher degree of road transport electrification can contribute to reducing the costs of climate mitigation policies.

Highlights

Abstract

Keywords

1. Introduction

2. Designing and calibrating public transport models

3. Optimal supply policies

4. Emerging technologies and related policies

5. Conclusions

CRediT authorship contribution statement

Acknowledgement

Appendix.

References

Abstract

Public transport provision requires substantial organisational efforts, careful planning, financial contributions from the public, and coordination between millions of passengers and staff members in large systems. Efficient resource allocation is critical in its daily operations. Therefore, public transport has been among the most popular subjects in transport economics since the infancy of this discipline. This paper presents an overview of the literature developed over the past half century, including more than 300 important contributions. With a strong methodological orientation, it collects, classifies, and compares the frequently used analytical modelling techniques, thus providing a cookbook for future research and learning efforts. We discuss key findings on optimal capacity provision, pricing, cost recovery and subsidies, externalities, private operations, public service regulation, and cross-cutting subjects, such as interlinks with urban economics, political economy, and emerging mobility technologies.

1. Introduction

Public transport, defined in this paper as high-capacity vehicle sharing with fixed routes and schedules, is the backbone of urban transport systems in global cities, especially in densely populated metropolitan areas. It is unlikely that mobility will become completely private in the near future, simply because of the inevitable traffic congestion and the difficulties of storing individual vehicles when they are not in use. In other words, even though technological development may transform the appearance of public transport, the fundamental challenge of coordinating between individual travellers who share vehicles of high capacity will remain. The purpose of public transport economics is to make this coordination more efficient, ensuring optimal resource allocation to unlock all societal benefits of mass mobility.

This work reviews more than 300 papers, including the most influential contributions that shaped our understanding of the economics of public transport over recent decades. The earliest studies date back to the 1960s and the 1970s when advanced quantitative methods were not available to calibrate disaggregate supply models, estimate sophisticated demand models, and simulate policy interventions’ impact on large urban networks. Did public transport economics significantly change over half a century? Interestingly, the main messages and policy recommendations of the economists in this field are still the same. Scale (density) economies, road pricing, substitution with underpriced car use, socially optimal subsidies, and the peak load problem are still on the research agenda in various forms, just like decades ago. However, the prevalence of popular subjects does not imply that theoretical and empirical results have achieved maximum impact on policymaking. Despite the surrounding consensus among members of the scientific community, the links between scale economies and subsidisation or the limitations of public transport pricing in congestion mitigation are not obvious in the wider transport industry, to mention only two examples. One of the challenges of public transport economics as a sub-discipline will emerge in knowledge dissemination and cross-fertilisation with related disciplines and professions. We believe that a critical overview of past research efforts is crucial in making impactful discoveries in the future.

Abstract
Video transmission in Internet of Vehicles (IoVs) is an emerging technology which utilizes the multimedia inside and outside vehicles itself through Vehicle Ad Hoc Networks (VANETs). In IoVs, traditional multi-hop routing protocols are not adaptive to mobile environment, especially the high-mobility driving environment in which vehicles need to accomplish video transmission under high quality of service (QoS). In this paper, we propose a multi-hop routing protocol for video transmission in IoVs based on cellular attractor selection (MRVT-CAS). We design a packet generation method for MRVT-CAS and use Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to construct the candidate set of next-hop selection. Then we map the expression of different genes in cell to selection of different next-hop nodes, and employ the mechanics of cellular attractor selection to select next-hop node. Moreover, we present a real-time feedback process to improve self-adaptability and robustness of routing protocol. Our simulation study compares MRVT-CAS with other routing protocols to evaluate performance of video transmission. The simulation results demonstrate the performance improvement over traditional methods, in terms of reachability, delay, stability and frame loss rate.

Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBraBIGSEA (Europe-Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workflows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, traffic jam estimation and trip recommendation based on estimated crowdedness. All developments are available under Open Source licenses (http://github.org/eubr-bigsea, https: //hub.docker.com/u/eubrabigsea/).

Introduction

Public transportation in large cities is a major source of high-valuable data to understand and improve the citizens’ lifestyle and to dynamically react to unplanned events. Multiple heterogeneous data sources are available, and different data analytics tools do exist. However, processing such data requires downloading the data, installing processing tools, managing the resources and developing processing software. EUBra-BIGSEA1 (Europe – Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) is a collaboration aimed at developing convenient data analytic services based on the cloud mainly tailored for public transportation data, able to process data under several restrictions, such as Quality of Service (QoS) constraints and privacy-awareness, by means of convenient and auto-parallelisable programming models. EUBra-BIGSEA has developed and implemented a software architecture that addresses a significant number of software requirements for three main use cases on public transportation data analysis.

1- Introduction

2- Related literature

3- Case study

4- Dynamic pricing

5- Load optimizer

6- Numerical experiments

7- Discussion

References

Abstract

We propose an heuristic approach to the vehicle ferry revenue management problem, where the aim is to maximize the revenue obtained from the sale of vehicle tickets by varying the prices charged to different vehicle types, each occupying a different amount of deck space. Customers arrive and purchase tickets according to their vehicle type and their willingness-to-pay, which typically increases over time because customers purchasing tickets closer to departure tend to accept higher prices. The optimization problem can be solved using dynamic programming but the possible states in the selling season are the set of all feasible vehicle mixes that fit onto the ferry. This makes the problem intractable as the number of vehicle types and ferry size increases. We propose a state space reduction, which uses a vehicle ferry loading simulator to map each vehicle mix to a remaining-space state. This reduces the state space of the dynamic program. Our approach allows the value function to be approximated rapidly and accurately with a relatively coarse discretization of states. We present simulations of the selling season using this reduced state space to validate the method. The vehicle ferry loading simulator was developed in collaboration with a vehicle ferry company and addresses real-world constraints such as manoeuvrability, elevator access, strategic parking gaps, vehicle height constraints and ease of implementation of the packing solutions.

Introduction

Vehicle ferries are used to transport passengers and their vehicles and, for many island populations, they can be the sole means of transporting goods, as well as providing a service to commuters and tourists. The global ferry market in 2012 was valued at over $15 billion, carrying more than 2 billion passengers and 350 million vehicles. Space on the vehicle deck is typically the binding constraint and managing prices to ensure efficient use of the available space is an important problem. In this article we describe an heuristic pricing algorithm that takes into account the efficiency of the packing, and practical considerations such as vehicle manoeuvrability when setting prices for different vehicle types, ranging from large freight vehicles to motorcycles. Customer arrivals into the booking system are stochastic and a customer will purchase a ticket with a probability dependent on the price, their vehicle type and the time left until departure. This assumption allows us to use price as a lever of demand to push towards more efficient and profitable vehicle mixes, where a vehicle mix is defined as the number of vehicles of each type. While this article is focused on vehicle ferries, a similar problem is encountered in other industries, e.g., sale of advertising time on radio or television channels, setting costs for bespoke manufacturing, and other freight transportation applications. We use dynamic programming to set prices, where the state of the dynamic program gives an indication of the space remaining on the ferry. To obtain an exact solution, the states of the dynamic program should correspond to the mix of vehicles that have already purchased tickets for the ferry and in other work we describe how to obtain exact solutions using a combination of mixed integer linear programming and dynamic pricing (see MartinezSykora, Currie, So, Bayliss, & Bennell, 2017).

Understanding passenger behaviors is of great importance in intelligent transportation and infrastructure planning. However, the passenger trajectories are actually complex temporal data, which consist of rich spatial and temporal information. What’s more, the observed passenger trajectories may be a mixture of different types of passengers with various travelling purposes. These difficulties make the prediction of passenger behaviors a challenging work. To address these problems, this paper improves the existing passenger behavior prediction methods from the following two aspects: 1) Encoding the travelling sequence with personalized semantic sensing, and 2) constructing multi-pattern prediction models to capture multiple travelling purposes and dynamics. Along this line, this paper provides a novel passenger behavior prediction model, namely, Semantic and multi-Pattern Long Short-Term Memory (SP-LSTM) model. Particularly, 1) a translation unit is designed, which is able to encode an observed travelling sequence into a structured sequence with consideration of individual travelling semantics; 2) a multi-pattern learning schematic is proposed, which first identifies the travelling patterns of passengers and then handles different patterns with different learning modules; 3) a unified learning framework is provided to integrate the semantic sensing module and multi-pattern learning module together, and present the final prediction results. To evaluate the proposed method, this paper conducts experiments on real-world passenger travelling data. Results demonstrate the superiority of SP-LSTM over both classical and the state-of-the-art methods.

Introduction

Public transportation plays an important role in daily life of residents, especially in metropolises such as New York, USA and Beijing, China. On the one hand, data mining and machine learning have undergone a rapid development in the recent years, machine-learning technology powers many aspects of modern society [1]; on the other hand, we have accumulated a large amount of transportation data, such as NYC Taxi Open Data [2], Uber Trip Data, Taxi Trip Records and so on. Hence, an opportunity of improving the public transportation service by data-driven solutions and strategies has been witnessed by many scholars such as [12]–[16]. This paper focuses on modelling passenger behaviors and predicting passengers’ next stations. By doing this, we can help the government and enterprises optimize to dispatch the transportation resources, and also help the citizens to circumvent the crowded stations. As shown in Fig. 1, If travelling behaviors of passengers can be predicted precisely, a better traffic route planning can be made for workers with regular moving patterns, such as P1 and P3, and recommended routes or tourism products can be provided for travelers shown as P2.

User satisfaction is a key indicator of public service quality, especially for those services considered basic necessities. The conceptualization and measurement of transport service quality—a fundamental determinant of demand—poses challenges for conducting economic analyses and designing mobility policies. Several operating companies are involved in the transport sector. In this paper, the bus network of the metropolitan area of Granada, Spain, is taken as a case study. The aim is to design a model of overall satisfaction based on level of satisfaction using a specific set of factors that take into account the individual characteristics of users and the differential effect of using different bus lines. A combined method using nonlinear principal component analysis (NLPCA) and a logit multilevel model (LMLM) in two steps is applied to a satisfaction survey conducted by the Metropolitan Transport Consortium of Granada (Consorcio de Transporte Metropolitano del área de Granada) in 2013. The survey shows that even though customers within the metropolitan area of Granada are satisfied with the service received (67.26%), the level of satisfaction is not equal for all bus lines, with the perceived quality of some lines being above or below the average. This differential effect is due to different reasons, including the technical and functional performance of the operating companies, commercial speed and length and type of route, among others. Both the operators and the public administration need to focus their attention on these lines in order to design economic policy measures to improve bus lines with below standard compliance.

Introduction

and literature review In order to create a new model of mobility and travel in more sustainable ways, city dwellers must modify their usual behaviour, especially within metropolitan areas (Miralles Guasch, 2002; Lizárraga, 2006). As regards public transport, significant changes are needed which cannot be achieved only by improving the efficiency of vehicle design and traffic management. Changes must also be made in the way transport is considered and how solutions are identified and evaluated (Litman, 2003). The aim of this study is to provide a design model to determine the perceived quality of the public transport service of the metropolitan area of Granada, Spain. To this end, a satisfaction survey of the bus network conducted in 2013 by the Metropolitan Transport Consortium of Granada was used as a case study. It is important to note that the interurban transport service of Granada is heterogeneous because it is managed by several operators with different lines or routes which provide service to fifty-two towns. The measurement and conceptualization of public transport quality is one of the greatest challenges of economic analyses and mobility policies given the importance of such data for both the companies that provide these services and the public administration (Román et al., 2014). Quality of service, as well as transport fares, price of petrol, personal disposable income, unemployment rates and vehicle ownership, have been considered essential factors that determine public transport demand (Paulley et al., 2006; Holmgren, 2007; Cordera et al., 2015). Public policies aimed at promoting the use of public transport as a means to reduce traffic jams and pollution must create a more appealing image focused on markets in order to make the this type of transport more competitive than private vehicles (Beirão and Cabral, 2007: 478; dell’Olio et al., 2010: 388). The study of public transport quality forms part of the field of service quality; an ambiguous concept at the crossroads of a wide range of attributes (Grönroos, 1984; Parasuraman et al., 1985; Hensher et al., 2003; Paulley et al., 2006; Beirão and Cabral, 2007; de Oña et al., 2016). For this reason, it opens up and interesting field of research with practical implications for transport suppliers and authorities.

The assessment of transit service quality is a matter of academic interest since over thirty years already, and several research studies highly covered the scientific literature since the beginning of the new millennium. The growing interest for this topic also from transit enterprises and authorities is due to the awareness that the knowledge of the transit service performances can be used for monitoring the services and for identifying potential weak points, with the aim to increase passenger’s satisfaction and finally attract new transit users and retaining the usual ones. In fact, it is well known that in recent years users’ travel behaviour has become more complex, and people prefer private car for moving, because it is perceived as the most comfortable, flexible, and fast mode compared with the other ones. The urban sprawl and low density areas rising also contributed to increase the attractiveness of car as the most convenient transportation mode from all the points-of-view, with undesirable consequences in terms of environmental pollution, energy consumption and quality of life (Nocera and Cavallaro, 2016; Ayyildiz et al., 2017). In order to reduce these problems, travel demand has to be moved from private car to more sustainable mode as public transit systems. Consequently, transport companies have to operate interventions aimed to reduce travel time and increase service quality over all the aspects, since improvements in service quality can have a significant positive impact on the willingness of potential users to choose public over private transport (de Oña et al., 2014; Eboli et al., 2016). Transit service quality can be measured by different approaches. The most common approach is based on transit users’ opinions about the used services, collected by means of surveys. When service quality is measured from the customer’s perspective, transit quality depends on the passengers’ perceptions about each attribute characterising the service (de Oña et al., 2013). In recent years, the most popular methods for capturing transit users’ perceptions were: (1) traditional Customer Satisfaction Surveys (CSS), where users express their opinions by rating the various service characteristics (Weinstein, 2000; Cavana et al., 2007; Joewono and Kubota, 2007; Pakdil and Aydın, 2007; Dell’Olio et al., 2011; Eboli and Mazzulla, 2010, 2012b; Jen et al., 2011; Habib et al., 2011); and (2) Stated Preference surveys, where the importance given by the users to the service attributes indirectly derives from exercises based on the stated preferences according to which users express their opinions by choosing (or rating of ranking) alternative hypothetical services (Cirillo et al., 2011; Eboli and Mazzulla, 2008a, 2008b, 2010; Hensher and Prioni, 2002; Hensher et al., 2003; Dell’Olio et al., 2011). An alternative approach entails using different variables of the transit system demand and operation to calculate the ‘efficiency’ indicators (e.g. Badami and Haider, 2007; Lao and Liu, 2009; Eboli and Mazzulla, 2012a). There are also some studies integrating the two mentioned approaches (e.g. Sheth et al., 2007; Abreha, 2007; Nathanail, 2008; Eboli and Mazzulla, 2011).

1.1. Background and motivation

One of the most challenging problems in transportation planning is shifting a significant number of private car users to public transport (PT) in a sustainable manner. The problem of improving PT patronage and its compatibility with user needs is multifaceted. However, one may rather intuitively assume that improving PT service reliability, from the user perspective, and reducing operations costs from the perspective of PT operators will lead to an increase in ridership and competiveness compared with private car use. For most PT systems, inter-route or intermodal transfer connections are important factors affecting service reliability. Timetable synchronization is a useful strategy used to reduce passenger transfer waiting time, provide a well-connected service and improve PT service reliability (Ceder, 2001,2016). Most previous studies (e.g., Daganzo, 1990; Bookbinder and Desilets, 1992; and Ceder et al., 2001) on the PT timetable synchronization problem have treated the problem separately, rather than coupled with, or isolated from, other operation planning activities, such as vehicle scheduling, transit assignment and network design. However, as pointed out by Ceder (2001, 2016), it is preferable that all operation planning activities can be planned simultaneously in order to exploit the system’s capability to the greatest extent, and thereby, maximize its productivity and efficiency. Some researchers (e.g. Ceder and Stern, 1984; Van den Heuvel et al., 2008; Ibarra-Rojas et al., 2014; Liu and Ceder, 2016a; and Liu et al., 2017) have focused on integrating the PT timetable synchronization problem (TSP) with the vehicle scheduling problem (VSP) in order to analyze the tradeoff between level of service and operations costs. Various new mathematical programming models have been developed to serve as the basis for this tradeoff analysis for the integrated timetable synchronization and vehicle scheduling (ITSVS) problem. To date, there are two main decision-making problems that have not yet been addressed regarding an integrated approach to simultaneously analyze timetable synchronization and vehicle scheduling tasks. First, almost all previous studies focused on optimizing operation parameters only, such as trip offset times (departure time of the first trip), route headways and vehicle trip chains. However, the effect of the changes of these operation parameters on PT users’ trip choice behavior which have a heavy impact on other planning activities, is not taken into consideration in the timetable optimization process. Second, there is a lack of effective and efficient solution methods for finding a set of Pareto-efficient solutions so as to help for the purpose of assisting in multi-criteria decision analysis from the perspectives of both PT users and operators. To bridge these gaps, this study addresses the integrated PT timetable synchronization and vehicle scheduling problem with transit assignment (ITSVS-TA) for tactical and operational planning purposes.

Research and statistical figures often conclude that traffic accidents are an iceberg for comprehending the discrepancies of traffic management and entire transportation systems. Figures detailing traffic accidents in Indonesia, as is the case in many other countries, show significantly high numbers and severity levels; these types of totals are also evident in Jakarta, the highest-populated city in the country. Indonesia now has a population of more than 230 million people, and the country’s economic growth has given rise to increases in transportation demand and vehicle ownership. Similar figures are also evident in Jakarta, the capital, which has a population of almost 10 million people. The figures are even higher in the daytime due to commuters from the outlying areas and some supporting cities (i.e., Jabodetabek). We predict that Jakarta may have a population of around 12 million people during the daytime. With a road ratio of only less than 7%, the high vehicle growth rate and poor public transportation contribute to high-density road use. This problematic situation is hard to resolve as more than 90% of the available transportation modes are road-based. Figure 1 shows the numbers of traffic accidents in Indonesia over the last ten years (2004–2013). The table also indicates some classifications of accident severity: fatalities, severe injuries, and light injuries. Although fatalities and severe injuries are much fewer compared to light injuries, their numbers and trends are still high.

Accessibility, the ease of reaching destinations, is a key land use and transport performance measure (Hansen, 1959) that has been used in various studies to assess the equitable distribution of public transport service in a region (Bocarejo and Oviedo, 2012; Foth et al., 2013; Golub and Martens, 2014; Guzman et al., 2017; Manaugh and El-Geneidy, 2012). Low accessibility to jobs has been shown to be related to higher risks of unemployment, especially in low-income areas (Korsu and Wenglenski, 2010). Furthermore, the absence of public transport in a neighborhood, even temporarily, can significantly impact unemployment rates in areas where people  depend on it to reach their desired destinations (Matas et al., 2010; Sari, 2015; Tyndall, 2015). Originally, accessibility studies have been conducted at the regional level to investigate the relationship between transport systems, land use development and economic indicators such as dwelling prices and labor outcomes (Coppola and Nuzzolo, 2011; Hansen, 1959; Levinson, 2007). To explore these economic trends at a macroscopic level, accessibility studies have typically considered the generalized costs of travel which include all available modes (Coppola and Nuzzolo, 2011). More recently, accessibility measures have been used to conduct social equity assessments. In such cases, studies have looked at accessibility in a more disaggregated manner, namely by focusing on a specific transportation mode and/or groups of users. For example, recent studies on accessibility and equity concentrated on the distribution of the public transport system for different income groups (Delmelle and Casas, 2012; Legrain et al., 2016). In this paper, accessibility by public transport for people in a wheelchair is considered. This specific population is studied because the public transport network that is available for a person in a wheelchair can be significantly different from the network available to the rest of the population, due to physical barriers such as stairs in subway stations or inaccessible buses. Such barriers and the associated difficulty with reaching employment opportunities impose significant challenges on a vulnerable group of people that have been found to be more likely to be unemployed or underemployed compared to the general population for a considerable time (BC Stats, 2009; Benoit et al., 2012; Lillie et al., 2013; Statistics Canada, 2016). To our knowledge, no previous study has considered the physical barriers present in a public transport network when measuring accessibility to jobs by public transport. Accordingly, this study presents a methodological approach to assess the level of accessibility to jobs using public transport for wheelchair users in the City of Toronto and the Island of Montreal, Canada on a typical weekday during the morning peak. We then contrast this level of accessibility with that of the general population, who we assume are able to access the entire public transport network. In particular, we examine job accessibility experienced by wheelchair users and the general population in the most socially vulnerable census tracts in both cities.