Core Indicators for Monitoring the Sustainable Urban Mobility Plan: A Case Study

Abstract

The primary objective of this paper is to validate core indicators for monitoring Sustainable Urban Mobility Plans (SUMPs), focusing on the Functional Urban Area in Southern Poland. Given the high concentration of the population in urban areas within the European Union (EU)—currently at 70.9% and expected to rise to 83.7% by 2050—there is an urgent need to address associated challenges such as congestion and the significant contribution of cities to transport-related greenhouse gas emissions. Effective urban mobility planning is, therefore, more critical than ever. This study aims to provide a methodological framework for determining baseline values for four essential monitoring indicators: road safety, accessibility to public mass transit, greenhouse gas emissions, and air quality. These indicators are crucial for the European Commission’s evaluation of SUMPs. By focusing on these aspects, the research aims to enhance the effectiveness of urban mobility plans, ensuring they contribute meaningfully to the EU’s transport, climate, health, and social goals as outlined in the European Green Deal and the Sustainable and Smart Mobility Strategy.

1. Introduction

Sustainable Urban Mobility Plans (SUMPs) are vital for local mobility planning in the EU. They are defined as ‘strategic plans designed to satisfy the mobility needs of people and businesses in cities and their surroundings for a better quality of life’, which ‘build on existing planning practices and show due consideration of integration, participation, and evaluation principlesl’ [1]. While SUMPs are typically associated with and implemented by cities, such plans can and have been utilised by regions, polycentric regions, small towns, and neighbourhoods.

The new EU urban mobility framework published in December 2021 in the Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee, and the Committee of the Regions marks the necessity of an enhanced approach to Sustainable Urban Mobility Plans (SUMPs). Introduced in 2013, Sustainable Urban Mobility Plans are the cornerstone of urban mobility in the European Union and aim to help solve mobility challenges for entire urban functional areas in synergy with spatial, energy, and climate plans [2,3,4].

According to the SUMP database, there are currently over 1000 SUMPs in place. However, it should be noted that it is challenging to determine the precise number of SUMPs as the database relies on self-reported data and some existing plans are grouped with other strategies and/or are not necessarily packaged as SUMPs. The SUMP concept aims to shift urban mobility practices from reliance on traffic-centred planning to a more interdisciplinary, people-centred demand management planning, and it emphasises the process of developing and implementing the plans. As such, it is built on eight key principles, namely:

• Improving accessibility and providing high-quality, sustainable mobility for the entire functional urban area;
• Cooperation, coordination, and consultation across institutional boundaries.
• Public participation and stakeholder involvement;
• Assessment of the current and future performance of the transport system;
• Defining a long-term vision and implementation plan;
• Integrated development of all relevant transport modes;
• Continuous monitoring and evaluation;
• Quality assurance.

These principles could guide the development and implementation of SUMPs. However, as these are not binding, each SUMP is unique and largely shaped by those involved in its development. In fact, a recent fact-finding study, which explored a sample of 125 EU cities, found that only 42% of the sampled cities had high compliance with the principles, 49% had medium compliance, and 9% had low compliance. This flexibility allows accounting for local needs and capacity. However, a lack of standardisation represents a possible risk in terms of the quality of SUMPs and makes comparing SUMPs in terms of effectiveness a challenge.

In its 2021 evaluation of the Urban Mobility Package, the European Court of Auditors (ECA) found several omissions by EU countries that prevent Sustainable Urban Mobility Plans from effectively contributing to the EU’s increasingly ambitious transport, climate, health, and social goals and commitments, as set out in the European Green Deal and the Sustainable and Smart Mobility Strategy. According to the authors of the ECA report, the implementation of Sustainable Urban Mobility Plans is uneven across and within Member States: many cities still do not have Sustainable Urban Mobility Plans, and among those that do, there are significant differences in their quality.

The evaluation of the Urban Mobility Package showed the following:

• The Package’s goals for reducing the negative environmental impact of urban transport have not been achieved;
• There is a lack of transfer of the Package into the decisions of the authorities of Member States at the national level and into the decisions of private entities;
• There is uneven implementation of sustainable mobility objectives in different Member States;
• There is a lack of widespread dissemination of SUMPs in EU cities;
• The effects of EU investments due to lack of linkage to SUMPs are minimal;
• There are no instruments to ensure the high quality of SUMPs and to ensure their effective implementation;
• There are no common tools for collecting mobility data necessary for monitoring SUMP implementation;
• There is a need for EU support to build competencies and share information in the development and implementation of SUMPs.

In its Communication on Urban Mobility, the European Commission recommends shifting the focus from improving traffic flow to reducing transportation’s negative impacts on the environment, climate, health, accessibility, and safety through coordinated territorial, inter-institutional, and thematic actions based on the concept of sustainable urban mobility planning (SUMP) implementation.

The primary objective of this paper is to validate core indicators for monitoring Sustainable Urban Mobility Plans (SUMPs). Core indicators have been defined and included in the Regulation underpinning the trans-European transport network (TEN-T). However, the European Commission still needs to specify the methodology for determining these indicators because the various SUMP documents lack consistency in the evaluation approach. This article defines these indicators based on the author’s SUMP preparation experience and available spatial and statistical data.

2. Materials and Methods

2.1. Monitoring the Progress of Implementation of Sustainable Urban Mobility Plans

Comprehensive data on the transportation system is needed to understand the progress achieved and to improve existing plans [5,6,7]. As local authorities take individual actions to meet EU policy goals, it is becoming increasingly clear that a common approach to monitoring and providing information is lacking [8,9,10,11,12].

As part of a pilot project, the European Commission developed and tested a set of Sustainable Urban Mobility Indicators (SUMI) in selected cities. These indicators examined traffic fatalities and injuries, greenhouse gas emissions and air pollutants, traffic congestion, traffic task sharing, and transportation accessibility. The feedback provided information on improving the set of indicators, mainly to ensure that the methodology for obtaining data and calculating these indicators is simplified. The need to provide support to cities, increase adequate resources in local authorities, and support for data acquisition was also identified. Additionally, the need for greater involvement of member states in the process was identified, as several member states are not systematically collecting relevant data. This is a fundamental issue for TEN-T urban hubs, which are important parts of the European transportation system.

By the end of 2025, the European Commission plans to launch a supporting action in the form of a program under the Connecting Europe Facility, targeting member states to collect data for harmonised mobility indicators to monitor the progress achieved by TEN-T urban nodes regarding sustainable urban mobility. The Commission plans to ensure the collection and transmission of data on greenhouse gases emissions, traffic congestion, the number of fatalities and severe injuries in traffic accidents, modal split, availability of mobility services, and urban air and noise pollution.

2.2. Core Indicators for Monitoring the Sustainable Urban Mobility Plan

The case study presented here concerns the process of developing a Sustainable Urban Mobility Plan for the Functional Urban Area (FUA) of the cities of Sanok and Lesko. The FUA was chosen because it represents medium-sized urban areas in Poland and the EU. This area faces typical challenges related to urban mobility, such as increasing vehicle registrations and emissions, making it an appropriate case study for evaluating SUMPs. The location is in southeastern Poland and includes 12 local government units (municipalities and counties). The total population of the area is 209,000 inhabitants. Sanok is the largest city within the FUA. The city’s infrastructure supports various modes of transportation, contributing to its role as a regional transit point. The Sanok FUA is characterised by its diverse transportation network, which includes public buses, regional rail services, and road connections that facilitate mobility across the area. However, the region faces challenges related to transportation emissions, air quality, and accessibility, which are focal points of the Sustainable Urban Mobility Plan (SUMP) 2030+.

The authors determined the baseline values of the core indicators for SUMP monitoring.

Table 1. Overview of indicators—definitions.

Indicator	Definition
Air pollution	Emission rate (kg PM2.5 eq. per year per 100 thousand inhabitants
Greenhouse gas emissions	Greenhouse gas emissions—CO2 in tons (eq.)—per year per 100,000 inhabitants
Road safety	Road deaths from all traffic accidents in the urban area per year Share of population with adequate access to mobility services (public transportation)
Access to mobility services	Share of population with adequate access to mobility services (public transportation)

defines the core monitoring indicators used in the case study.

3. Results

3.1. Greenhouse Gas and Air Quality Indicators

Linear emissions of pollutants from transportation modes significantly impact air quality [13,14,15]. The most important source of linear emissions is private car transportation. Substances emitted by internal combustion engine vehicles affect the state of the air, especially in the immediate vicinity, and their impact decreases with distance [16,17,18,19,20].

The authors obtained data covering 2017–2021 on the number of vehicles registered in the analysed functional area from the Local Data Bank, CSO. In the summary of the number of registered vehicles published in the Local Data Bank, CSO considers the division by county when distinguishing the type of fuel used. In the case of information for Sanok County, the data come directly from the CSO database, and in the case of Lesko Municipality, the number of vehicles was recalculated concerning the number of residents of the municipality, thus obtaining the estimated number of vehicles in the area in question.

Table 2. The number of vehicles registered in the FUA, from 2017–2021.

Vehicle	2017	2018	2019	2020	2021
Passenger cars	44,285	46,189	47,799	49,478	51,010
Motorcycles	5617	5881	6289	6667	6857
Trucks	5351	5554	5781	5986	6217
Buses	220	219	216	224	231
Tractor units	355	389	419	426	465
SUM	55,828	58,232	60,504	62,781	64,780

summarises the number of vehicles registered in the functional area, including Sanok County and part of Lesko County, from 2017–2021.

The indicators shown in

Table 3. Calorific value and CO₂ emission factors for transportation fuels [8].

Fuel Type	Caloric Value [GJ/kg]	Emission Factor [Mg/GJ]
Liquid gas	0.0473 [GJ/kg]	0.0631 [Mg/GJ]
CNG	1.795 [kg/m3]	0.0477 [kg/m3]
Gasoline	0.0443 [GJ/kg]	0.0693 [Mg/GJ]
Diesel fuel	0.043 [GJ/kg]	0.0741 [Mg/GJ]

and

Table 4. Emission factors for PM10 and PM2.5 particulate matter [9].

Vehicle	Emission Factor PM10 [g/pcs·km]	Emission Factor PM2.5 [g/pcs·km]
Passenger car	0.014	0.013
Truck up to 3.5 t	0.1293	0.116
Truck	0.558	0.502
Bus	0.611	0.55
Bus CNG	0.01	0.01

determined the volume of fuel consumption and the emissions of gaseous and particulate pollutants into the atmosphere.

PM10 and PM2.5 particulate matter emissions were determined based on the indicators shown in Table 4.

The analysis of the volume of transportation fuel consumption in the functional area takes into account private transportation (which includes motorcycles and cars), commercial transportation (which includes trucks and tractor-units), and public transportation (buses). For the calculations, the authors adopted the following averaged values of annual vehicle mileage (within the administrative boundaries of the analysed area):

• Average annual mileage of a motorcycle—4000 km/year;
• Average annual mileage of a passenger car—7000 km/year;
• Average annual mileage of trucks—10,000 km/year;
• Average annual mileage of a bus—40,000 km/year.

Data published in the Local Data Bank, the Central Statistical Office (GUS), allow for the calculation of the volume of consumption of transport fuels within the functional area covering Sanok County and a part of Lesko County. Based on this, the amount of gas and particulate pollution emissions generated by private, commercial, and public transport was estimated for 2017–2021. The results are presented in Figure 1 and Figure 2.

The analysis was performed, and the results obtained made it possible to determine the projected directions of changes in the demand for transportation fuels and estimate the volume of emissions from the transportation sector. The forecast was made until 2030, taking into account two variants:

• Variant I refers to the demographic situation and the projected decline in the number of residents of the analysed area, as well as a change in the structure of the fleet towards low- and zero-emission vehicles and a shift in transportation behaviour resulting, for example, from a significant improvement in the offer of public transportation.
• Variant II assumes that the growth rate in the number of vehicles in the analysed area will continue at a similar rate.

Figure 3, Figure 4 and Figure 5 show projected changes in the volume of CO₂ and particulate emissions they generate into the air.

The analysis carried out concerning the emission of gaseous and particulate pollutants into the air generated by transport in the functional area covering the Sanocki district and part of the Lesko district allows for the following conclusions:

• The number of registered vehicles on the territory of Sanok and Lesko in the last five years is dynamically increasing;
• With the increase in the number of vehicles in traffic, the emission of CO₂, PM10, and PM2.5 particulate matter into the atmosphere increases proportionally;
• The share of vehicles using gasoline and diesel is similar;
• The largest share of the site’s total linear emissions comes from private transportation, which includes passenger cars and motorcycles;
• The smallest share in the total linear emissions of the study area is public transportation;
• Analysing past growth trends, it is more likely that there will be an increasing demand for energy from transportation fuels, which will, in turn, increase emissions of particulate and gaseous pollutants into the atmosphere.

Table 5. Values of CO₂ emission and air quality indicators (PM2.5) for FUA.

Time Horizon	Indicator CO2 [Mg/Year]	Indicator PM2.5 [Mg/Year]
Baseline value for 2023	70,212.65	42.03
Target value for 2030	64,051.97	38.35

shows the proposed values of greenhouse gas emissions and air quality indicators per 100,000 inhabitants per year.

The target values of the indicators were adopted after consultations and agreements with local government units.

3.2. Road Safety Indicator

According to accepted definitions, a traffic accident is an event related to the movement of vehicles on public roads that results in the death or injury of road users. A person who dies as a result of injuries sustained on the spot or within 30 days is considered a fatal victim of a traffic accident. An injured victim is considered a person who received medical attention as a result of the accident.

The road safety indicator (FR), defined as the number of traffic accident fatalities per year/100,000 inhabitants, was determined from Formula (1).

$$FR=\frac{{\sum }_i{K}_i\ast 100,000}{C_{ap}},$$

(1)

where FR is the mortality rate per 100,000 inhabitants of the functional area per year, K_i is the number of people killed in the means of transport per year, Cap is the number of inhabitants in the study area, and i is the type of means of transport.

This publication adopted data on mortality rates per population for Sanok and Lesko counties, taken from the Local Data Bank of the Central Statistical Office in Poland (CSO). The analyses adopted values from 2011–2021, so the authors conducted all analyses over 11 years. The CSO publishes data on road safety at the district administration level in its Local Data Bank. Recalculated indicators concerning the number and structure of the population (gender, age groups) since 2010 by the balance sheet were prepared based on the 2011 and 2021 National Censuses results.

Figure 6 illustrates the values of the indicator for Sanok and Lesko counties against the values of the indicator in the whole country. One should note a clear downward trend in the indicator concerning the entire country’s area. The graph in Figure 7 shows the average fatality rate per 100,000 population in 2011–2021. The trend indicates an increasing character—for 2030, the projection of the FR indicator value is about 7.2.

Table 6. Road safety indicator values for SUMP.

Time Horizon	FR
Baseline value for 2023	5.51
Target value for 2030	5.51

shows the proposed values of the “fatalities per 100,000 inhabitants” indicator for monitoring under the SUMP for the Functional Urban Area of the cities of Sanok and Lesko (baseline for 2023 and target for 2030).

The baseline value for 2023 was estimated based on the latest official data (i.e., for 2021). The target value determines the expected stop of the upward trend, considering the projected increase in total road traffic. With the implementation of measures promoting sustainable mobility, road safety solutions, and a reduction in the severity of accidents and, therefore, the number of fatalities, the indicator’s value is assumed to be maintained at the same level, which is a most realistic scenario.

3.3. Access to Mobility Services

Access to mobility services is one of the determinants of the quality of life of residents of cities and functional areas [21]. The authors in the works [22,23] prove, among other things, that shaping the pedestrian accessibility of bus stops increases the potential of the transport system.

The European Commission introduced a measure of transport accessibility as one of the Sustainable Urban Mobility Indicators (SUMI). Transport accessibility is defined as the share of the population with adequate access to mobility services, in this case, public transport. The value of the indicator is taken on a 10-point scale, with 0 being the lowest level of access to mobility services (0% of the population with adequate access to public transportation) and 10 being the highest level (100% of the population with adequate access to public transportation). The value of this indicator is influenced by both the distance from bus stops/stations/hubs and the departures of the public transportation service. The access to mobility services indicator takes into account the following criteria for accessibility levels:

• Number of inhabitants having more than a 5-min walk (417 m in a straight line) to a bus/tram/trolleybus stop or similar means of public transportation AND more than a 10-min walk (833 m in a straight line) to a rail/metro station;
• Number of inhabitants having less than 5 min of travel (417 m in a straight line) to a bus/tram/trolleybus stop or similar means of public transportation with an average of fewer than four connections per hour OR less than 10 min of travel (833 m in a straight line) to a rail/metro station with an average of fewer than four connections per hour;
• Number of inhabitants having less than a 5-min walk (417 m in a straight line) to a bus/tram/trolleybus stop or similar public transportation with an average number of connections of between four and ten per hour OR less than a 10-min walk (833 m in a straight line) to a rail/metro station with an average number of connections of between four and ten per hour;
• Number of inhabitants having less than a 5-min walk (417 m in a straight line) to a bus/tram/trolleybus stop or similar means of public transportation with an average number of connections above 10 per hour OR less than a 10-min walk (833 m in a straight line) to a rail/metro station with an average number of connections above 10 per hour;
• Number of inhabitants having less than a 5-min walk (417 m in a straight line) to a bus/tram/trolleybus stop or similar public transportation with an average number of connections above 10 per hour AND less than a 10-min walk (833 m in a straight line) to a rail/metro station with an average number of connections above 10 per hour. (See

  Table 7. Classification of accessibility by frequency of service and distance from the stop.

  	Rail Transport
  Bus transport	Distance **		≤833 m (10 min. walking)			>833 m (10 min. walking)
  		Frequency * (departures/h)	≥10 departures/hour	≥4 and <10 departures/h	<4 departures/h	≥10 departures/h	≥4 and <10 departures/h	<4 departures/h
  	≤417 m (5 min walking)	≥10 departures/h	Very good access	Very good access	Very good access	Very good access	Very good access	Very good access
  		≥4 and <10 departures/h	Very good access	Good access	Good access	Good access	Good access	Good access
  		<4 departures/h	Very good access	Good access	Sufficient access	Sufficient access	Sufficient access	Sufficient access
  	>417 m (5 min walking)	≥10 departures/h	Very good access	Good access	Sufficient access	No access	No access	No access
  		≥4 and <10 departures/h	Very good access	Good access	Sufficient access	No access	No access	No access
  		<4 departures/h	Very good access	Good access	Sufficient access	No access	No access	No access

  *—Average number of departures per hour on a normal weekday, considering the period between 6:00 and 20:00; **—Straight-line distance, there is no requirement for estimating the value on the basis of a walking distance via streets.

  ).

For cities with more than 100 thousand inhabitants, the access is considered very good in situations of more than the average of 10 connections per hour from 6:00 a.m. to 8:00 p.m. (total in the group of stops within range); good access is when there are more than 4 connections per hour on average. For cities with less than 100,000 residents—which is the case for the analysed functional area—the access is considered very good for situations of more than four connections per hour from 6:00 a.m. to 8:00 p.m.; good access is public transportation-served stops providing less than an average of four connections per hour. Table 3 shows the accessibility classification by frequency of service and distance from the stop.

Based on an analysis of the public transport timetables (buses, rail), no bus or rail stops were identified for the criteria, assuming a frequency expressed in terms of the average number of connections in the range of more than 10 per hour.

The indicator for access to mobility services was determined from the Formula (2).

$$Accl=\frac{{\sum }_{i}P{R}_i\ast W_i}{C_{ap}}$$

(2)

where Accl is the access to mobility services indicator [%], PR_i is the number of people living in the access typology zone and defined by a combination of the level of PT accessibility, and W_i is the weight to determine whether accessibility to mobility services is adequate (depending on the combination of public transportation accessibility criteria). The weighting varies for small (i.e., less than 100,000 residents) or large urban areas; C_ap is the number of inhabitants in the study area.

The W_i weight is predefined and identifies whether availability is appropriate as follows:

• W_i = 1, when availability is fully adequate;
• W_i = 0.5, when it is not fully adequate;
• W_i = 0, when it is not adequate.

The authors used the Geostatistical Portal—Spatial Statistical Data database and the lists of bus stops provided by the functional area’s local government units to determine the values for the indicator of access to mobility services. Table 4 shows the values of the Accl indicator for each local government unit. (See

Table 8. Access to mobility services indicator values for FUA cities and communities.

Community/City	Cap	PR1	Wi	PR2	Wi	PR3	Wi	Accl	[%]
City of Sanok	35,178	0.0	0.0	30,606	0.5	4572	1.0	5.65	56
City of Lesko	5758	4823	0.0	935	0.5	0.0	1.0	2.03	20
City of Zagórz	4905	1048	0.0	3857	0.5	0.0	1.0	3.93	39
Besko	4415	1575	0.0	2840	0.5	0.0	1.0	3.22	32
Bukowsko	5349	3064	0.0	2285	0.5	0.0	1.0	2.14	21
Komańcza	4500	2606	0.0	1894	0.5	0.0	1.0	2.10	21
Lesko	11,169	8881	0.0	2288	0.5	0.0	1.0	1.02	10
Sanok	18,161	8677	0.0	9484	0.5	0.0	1.0	2.61	26
Tyrawa Wołoska	1950	827	0.0	1123	0.5	0.0	1.0	2.88	29
Zagórz	12,752	8349	0.0	4403	0.5	0.0	1.0	1.73	17
Zarszyn	8936	3754	0.0	5182	0.5	0.0	1.0	2.90	29

).

The average percentage value of the functional area’s access to mobility services indicator is 27%.

Table 9. Access to mobility services indicator values for SUMP.

Time Horizon	Accl [%]
Baseline value for 2023	27
Target value for 2030	30

shows the proposed values of the access to mobility services indicator for monitoring under the SUMP for the Functional Urban Area of Sanok and Lesko (baseline for 2023 and target for 2030).

The low value of the access to mobility services indicator results from the functional urban area’s low population density and underdeveloped public transport network. Population density and the number and frequency of connections determine this indicator’s value. The only exception to the whole FUA is the city of Sanok, which has its own public transportation.

The target percentage of accessibility to mobility services was adopted after consultations and agreements with local government units and after analysing the potential public transport system development in the considered functional area. It should be noted that the methodology for determining the above indicator for assessing the transportation accessibility of functional areas requires access to specialised GIS software. This requires qualified staff or outsourcing the work of determining this indicator. Due to costs, the above rationale may hinder its monitoring in the case of smaller local governments.

4. Conclusions

This paper provides a comprehensive framework for validating core indicators for monitoring Sustainable Urban Mobility Plans (SUMPs) within the Sanok Functional Urban Area (FUA) case study.

Our research demonstrates an example of the data collection and analysis methodology by conducting a detailed case study of the Sustainable Mobility Plan 2030+ for a representative functional urban area in southeastern Poland. This includes the assessment of transportation-related emissions, air quality, and public transit accessibility, which are pivotal in addressing the mobility challenges posed by urbanisation and its environmental impact. The findings highlight the pressing issue of the dynamic increase in vehicle registrations and their consequent emissions, underscoring the urgent need for transitioning towards low- and zero-emission vehicles and enhancing public transportation services. The study also projects future trends in emissions, providing a clear direction for sustainable urban mobility planning. The practical implications of this research are significant for local authorities and policymakers. It offers actionable insights into improving urban mobility systems, ensuring alignment with the EU’s sustainability and climate goals. Furthermore, the proposed indicators facilitate continuous monitoring and evaluation, enabling timely interventions to optimise the effectiveness of SUMPs.

In conclusion, this paper addresses the critical need for standardised monitoring indicators in sustainable urban mobility. It sets a precedent for future studies to enhance urban mobility planning and its implementation across various functional urban areas. By focusing on the specific objectives of reducing emissions, improving transport accessibility, and enhancing road safety, this research contributes substantially to the broader goal of sustainable urban development. However, our research has certain limitations. This paper was conducted within the institutional framework of Poland, so the validity of some results primarily pertains to countries with similar historical development in city planning.