# Transportation Deployment Casebook/2025/The Life Cycle of Japan's National Railways: JNR to JR
## Introduction

The Japanese National Railways (JNR), was the public corporation that operated Japan's national railway network from 1949 to 1987. After World War II, Japan experienced rapid economic growth, leading to an increased demand for transportation development. In response, the Japanese National Railways (JNR) expanded its national railway network at a rapid pace. By 1964, the inauguration of the Shinkansen between Tokyo and Osaka marked a historical peak for JNR. However, ironically, it was at this point that JNR began to experience financial deficits. These deficits continued to escalate throughout the 1970s and 1980s. By around 1980, the annual fiscal deficit accounted for approximately 30% of revenue, amounting to nearly 1.0084 trillion yen. Additionally, JNR had accumulated long-term debt of approximately 25 trillion yen. Due to political and economic considerations, the Japanese government decided to privatize JNR on April 1, 1987, restructuring it into six regional JR passenger railway companies and one nationwide JR freight railway company. This study mainly conducts a qualitative analysis of the life cycle of JNR, focusing on its establishment, development, and dissolution. Additionally, a curve fitting analysis is performed on two key transportation metrics: passenger-kilometer performance and total passenger volume.

## Qualitative Analysis
### Early Development and Nationalization (1872–1949)

In 1870, with the support of British engineering expertise, the Japanese government initiated the construction of its first railway line, which commenced operations between Tokyo and Yokohama in 1872. This event marked the beginning of Japan's railway era. By 1905, the government had expanded the railway network from 29.0 kilometers in 1872 to 2,562 kilometers. Simultaneously, as the government permitted private companies to construct and operate railway lines, the total railway length rapidly increased to 5,231 kilometers.
Following the Russo-Japanese War (1904-1905), recognizing the strategic importance of rail transport, the Japanese government enacted the Railway Nationalization Act in 1906. This legislation mandated the nationalization of all railway lines except those serving exclusively local regions. By 1907, the government had completed the acquisition of private railway lines, consolidating the majority of the national railway network under state control, resulting in a government-operated railway system with a total length exceeding 7,000 kilometers. By 1940, this figure had reached 18,000 kilometers.

### The Establishment and Development of JNR (1950-1964)

After the end of World War II, both the Japanese National Railways (JNR) and the national economy were in a state of collapse. Consequently, from 1945 to the reorganization of publicly listed companies in 1949, the shifts in government policy regarding the national railway reflected the broader administrative instability of the country. To reduce government expenditures and stabilize the national economy, the Japanese National Railways (JNR) was officially established on June 1, 1949. As an independently operated, state-owned public corporation, JNR was regulated by the Ministry of Transport (now the Ministry of Land, Infrastructure, Transport, and Tourism, MLIT). Its primary responsibilities included railway operations, infrastructure development, and the management of both conventional railway networks and future high-speed rail projects.
However, despite its legal status as an independent public corporation, JNR was, in practice, politically controlled by members of the National Diet, who sought to leverage its operations for political gain. The misalignment between political interests and the long-term strategic development of the national railway system created governance challenges, ultimately laying the foundation for the inefficiencies and structural weaknesses that contributed to JNR's eventual dissolution. Nevertheless, due to the strong demand for transportation in the 1950s and the irreplaceable role of railways in freight and passenger transport, the JNR system entered a period of rapid expansion. From 1950 to 1965, JNR's passenger volume increased by 150 percent, reflecting the railway's crucial role in Japan's post-war economic recovery.
In 1964, the completion and inauguration of the Tokaido Shinkansen marked a historical peak in JNR's development. However, this milestone also signaled the beginning of a worsening financial crisis, as long-standing budget deficits became increasingly severe. Persistent political interference, coupled with unsustainable financial policies, ultimately trapped JNR in a vicious cycle of debt and inefficiency, making recovery increasingly unattainable.

### JNR's Financial Decline and Dissolution (1965-1987)

In the mid-1960s, JNR's seemingly stable financial condition and expanding railway network masked the underlying threats posed by the rapid development of the highway system and managerial misjudgments within JNR. Both the government and the Ministry of Transport believed that JNR could continue to improve trunk and commuter services while further expanding the national railway network. However, the lack of objective analysis led to the failure of newly constructed railway lines, with the expansion into rural areas resulting in significant financial losses and escalating debt.
Between 1968 and 1980, more than 40 percent of the railway lines constructed by JNR were rural lines, yet these lines accounted for only five percent of the total transport volume. Additionally, the JNR Reconstruction Plan, proposed by Prime Minister Tanaka in 1973, included the construction of the Jōetsu Shinkansen connecting Tokyo to Tanaka's hometown of Niigata. This project, completed in the 1980s, further contributed to JNR's continued operational deficits. At the same time, rising inflation due to the economic crisis forced JNR to continuously increase fares. While fare hikes provided some relief to JNR's financial crisis, they also accelerated the decline in passenger numbers. In addition, factors such as JNR's excessive workforce and persistent labor-management conflicts played a crucial role in the organization's downfall. Ultimately, the fate of JNR was already sealed.
By the early 1980s, the Japanese government reached the conclusion that the only solution for the sustainable development of Japan's railway system was the dissolution and privatization of JNR. Consequently, in April 1987, the Japan Railways (JR) Group was established. Based on policy research on privatization, the government decided that JNR should be divided into smaller, more manageable regional entities, each operating independently. JNR was dissolved and divided into six regional passenger railway companies (JR East, JR West, JR Central, JR Hokkaido, JR Shikoku, and JR Kyushu) and one nationwide freight railway company (JR Freight). These JR companies initially remained government-owned but gradually transitioned towards privatization. This restructuring marked the end of Japan's nationalized railway system and the beginning of the JR era.

### JR Era (1987–Present)

In the 1990s, several JR companies, including JR East, JR Central, and JR West, gradually underwent privatization and became publicly traded entities. However, other JR companies, such as JR Hokkaido and JR Shikoku, remained partially government-owned. In 2002, JR East achieved full privatization, marking a significant milestone in Japan's railway history. Concurrently, new Shinkansen lines, including the Hokuriku, Kyushu, and Hokkaido Shinkansen, were developed and expanded, further enhancing the country's high-speed rail network.
Although technologies such as magnetic levitation (MAGLEV) trains and supersonic aircraft have generated enthusiasm for the future of transportation, Ryohei Kakumoto, an early advocate of JNR privatization in the late 1970s, maintains a conservative stance on the future development of Japan's railway system. Given Japan's geographical constraints, advanced technologies may not fundamentally transform the structure of its transportation network. As a densely populated island nation, Japan faces multiple limitations in transportation development. Consequently, the country's transportation system stabilized in the late 20th century, making dramatic changes in the 21st century unlikely.
Drawing lessons from the failure of JNR, future large-scale railway investments in Japan should be subject to thorough economic feasibility assessments rather than being driven solely by political considerations. The Japanese government must exercise caution in overexpanding transportation infrastructure to prevent unsustainable financial burdens. Moving forward, transportation policy should strike a balance among technological innovation, financial sustainability, and environmental impact, ensuring the long-term stability and sustainability of Japan's railway network.

## Quantitative Analysis
### Methodology

The logistic function is commonly used to model transport system growth, as it captures the birthing, growth, and maturity stages of technological or infrastructure development. The growth process of JNR's Passenger-kilometres and Passengers carried closely resembles the evolution of an S-curve. In the initial stage, both metrics grew rapidly but eventually stabilized due to factors such as market saturation, infrastructure constraints, and policy changes. Therefore, the S-curve (Logistic Function) can be used to quantitatively analyze JNR's lifecycle changes by examining Passenger-kilometres and Passengers carried.The three-parameter logistic function used to model the life cycle follows the standard form:
$S(t)={\frac {S_{max}}{[1+exp(-b(t-t_{i})]}}$
Where:
* $S(t)$ = Status measure (e.g., Passenger-km or Passengers carried)
* $t$ = Time (year)
* $S_{max}$ = Saturation level (maximum value the system approaches)
* $t_{i}$ = Inflection point (year when system reaches 50% of Smax​)
* $b$ = Growth rate
To determine the best-fitting parameters, I followed a two-step process:
First, transform the logistic function into a linear regression model by taking the natural logarithm:
$Y=\ln \left({\frac {S_{\max }-S(t)}{S(t)}}\right)$
where：
* $Y$ is the transformed dependent variable
* $X$ (independent variable) = year.
* $b$ is the slope of the regression
* $-b{t_{i}}$ is the intercept
Second, estimate Smax using trial values and select the one that maximizes $R^{2}$, indicating the best goodness of fit.

### Results

During the fitting process, it was found that the data from 2020 to 2023 exhibited significant fluctuations due to the impact of COVID-19, greatly affecting the fitting results. Therefore, fitting analyses were conducted separately for cases that included and excluded these four years of data.

#### Curve Fitting for Passenger-kilometres

* including 2020-2023 data:
1905-2023 Passenger-kilometres Data Table_1 :
| Year | Passenger-km (Million) | Predicted Data (Million) |
| 1905 | 1521 | 5898.74 |
| 1906 | 1971 | 6253.61 |
| 1907 | 3787 | 6629.32 |
| 1908 | 4415 | 7027.03 |
| 1909 | \ | \ |
| 1910 | 4890 | 7893.39 |
| 1911 | 5444 | 8364.65 |
| 1912 | 5836 | 8863.14 |
| 1913 | 5940 | 9390.33 |
| 1914 | 5832 | 9947.74 |
| 1915 | 6206 | 10536.97 |
| 1916 | 6848 | 11159.68 |
| 1917 | 8876 | 11817.60 |
| 1918 | 10572 | 12512.53 |
| 1919 | 12782 | 13246.33 |
| 1920 | 13493 | 14020.95 |
| 1921 | 14319 | 14838.39 |
| 1922 | 15663 | 15700.71 |
| 1923 | 17170 | 16610.05 |
| 1924 | 18106 | 17568.60 |
| 1925 | 18741 | 18578.64 |
| 1926 | 19245 | 19642.45 |
| 1927 | 20055 | 20762.42 |
| 1928 | 21595 | 21940.95 |
| 1929 | 21355 | 23180.48 |
| 1930 | 19885 | 24483.49 |
| 1931 | 19125 | 25852.49 |
| 1932 | 19005 | 27289.99 |
| 1933 | 20825 | 28798.50 |
| 1934 | 22575 | 30380.53 |
| 1935 | 24175 | 32038.56 |
| 1936 | 26225 | 33775.02 |
| 1937 | 29055 | 35592.31 |
| 1938 | 33635 | 37492.72 |
| 1939 | 42065 | 39478.48 |
| 1940 | 49345 | 41551.68 |
| 1941 | 55555 | 43714.29 |
| 1942 | 60455 | 45968.12 |
| 1943 | 74075 | 48314.78 |
| 1944 | 77285 | 50755.70 |
| 1945 | 76035 | 53292.05 |
| 1946 | 87455 | 55924.76 |
| 1947 | 91165 | 58654.46 |
| 1948 | 82005 | 61481.48 |
| 1949 | 69665 | 64405.82 |
| 1950 | 69004 | 67427.10 |
| 1951 | 79045 | 70544.58 |
| 1952 | 80485 | 73757.11 |
| 1953 | 83555 | 77063.11 |
| 1954 | 87045 | 80460.59 |
| 1955 | 91239 | 83947.10 |
| 1956 | 98085 | 87519.72 |
| 1957 | 101245 | 91175.08 |
| 1958 | 106215 | 94909.38 |
| 1959 | 114195 | 98718.31 |
| 1960 | 123983 | 102597.17 |
| 1961 | 131755 | 106540.80 |
| 1962 | 141195 | 110543.64 |
| 1963 | 152715 | 114599.75 |
| 1964 | 164185 | 118702.84 |
| 1965 | 174014 | 122846.30 |
| 1966 | 175765 | 127023.25 |
| 1967 | 184315 | 131226.58 |
| 1968 | 184815 | 135448.98 |
| 1969 | 181525 | 139683.02 |
| 1970 | 189726 | 143921.19 |
| 1971 | 190321 | 148155.93 |
| 1972 | 197829 | 152379.74 |
| 1973 | 208097 | 156585.16 |
| 1974 | 215564 | 160764.87 |
| 1975 | 215289 | 164911.76 |
| 1976 | 210740 | 169018.91 |
| 1977 | 199653 | 173079.68 |
| 1978 | 195844 | 177087.75 |
| 1979 | 194690 | 181037.15 |
| 1980 | 193143 | 184922.28 |
| 1981 | 192115 | 188737.95 |
| 1982 | 190767 | 192479.40 |
| 1983 | 192906 | 196142.31 |
| 1984 | 194180 | 199722.81 |
| 1985 | 197463 | 203217.49 |
| 1986 | 198299 | 206623.39 |
| 1987 | 204677 | 209938.04 |
| 1988 | 217589 | 213159.37 |
| 1989 | 222671 | 216285.78 |
| 1990 | 237657 | 219316.09 |
| 1991 | 247035 | 222249.50 |
| 1992 | 249615 | 225085.61 |
| 1993 | 250025 | 227824.40 |
| 1994 | 244385 | 230466.14 |
| 1995 | 248998 | 233011.46 |
| 1996 | 251725 | 235461.24 |
| 1997 | 247655 | 237816.65 |
| 1998 | 242807 | 240079.09 |
| 1999 | 240877 | 242250.14 |
| 2000 | 240659 | 244331.62 |
| 2001 | 241133 | 246325.45 |
| 2002 | 239246 | 248233.74 |
| 2003 | 241160 | 250058.70 |
| 2004 | 242300 | 251802.61 |
| 2005 | 245996 | 253467.86 |
| 2006 | 249029 | 255056.88 |
| 2007 | 255210 | 256572.15 |
| 2008 | 253556 | 258016.18 |
| 2009 | 244247 | 259391.47 |
| 2010 | 244593 | 260700.54 |
| 2011 | 246937 | 261945.90 |
| 2012 | 253788 | 263130.02 |
| 2013 | 260013 | 264255.35 |
| 2014 | 260097 | 265324.31 |
| 2015 | 269394 | 266339.27 |
| 2016 | 271996 | 267302.54 |
| 2017 | 275124 | 268216.38 |
| 2018 | 277670 | 269083.00 |
| 2019 | 271936 | 269904.54 |
| 2020 | 152084 | 270683.07 |
| 2021 | 170190 | 271420.61 |
| 2022 | 217509 | 272119.09 |
| 2023 | 248348 | 272780.39 |
Result_1 :
| $S_{max}$ | 284000 |
| $b$ | 0.0597 |
| $t_{i}$ | 1969.5467 |
| $R^{2}$ | 0.9209 |
* Excluding 2020-2023 data:
1905-2023 Passenger-kilometres Data Table_2 :
| Year | Passenger-km (Million) | Predicted Data (Million) |
| 1905 | 1521 | 4894.44 |
| 1906 | 1971 | 5215.94 |
| 1907 | 3787 | 5558.15 |
| 1908 | 4415 | 5922.32 |
| 1909 | \ | \ |
| 1910 | 4890 | 6722.04 |
| 1911 | 5444 | 7160.49 |
| 1912 | 5836 | 7626.76 |
| 1913 | 5940 | 8122.49 |
| 1914 | 5832 | 8649.42 |
| 1915 | 6206 | 9209.39 |
| 1916 | 6848 | 9804.31 |
| 1917 | 8876 | 10436.19 |
| 1918 | 10572 | 11107.14 |
| 1919 | 12782 | 11819.34 |
| 1920 | 13493 | 12575.09 |
| 1921 | 14319 | 13376.78 |
| 1922 | 15663 | 14226.87 |
| 1923 | 17170 | 15127.94 |
| 1924 | 18106 | 16082.66 |
| 1925 | 18741 | 17093.76 |
| 1926 | 19245 | 18164.10 |
| 1927 | 20055 | 19296.57 |
| 1928 | 21595 | 20494.16 |
| 1929 | 21355 | 21759.92 |
| 1930 | 19885 | 23096.95 |
| 1931 | 19125 | 24508.40 |
| 1932 | 19005 | 25997.44 |
| 1933 | 20825 | 27567.27 |
| 1934 | 22575 | 29221.07 |
| 1935 | 24175 | 30962.00 |
| 1936 | 26225 | 32793.21 |
| 1937 | 29055 | 34717.73 |
| 1938 | 33635 | 36738.56 |
| 1939 | 42065 | 38858.52 |
| 1940 | 49345 | 41080.33 |
| 1941 | 55555 | 43406.49 |
| 1942 | 60455 | 45839.31 |
| 1943 | 74075 | 48380.83 |
| 1944 | 77285 | 51032.82 |
| 1945 | 76035 | 53796.69 |
| 1946 | 87455 | 56673.53 |
| 1947 | 91165 | 59663.99 |
| 1948 | 82005 | 62768.28 |
| 1949 | 69665 | 65986.16 |
| 1950 | 69004 | 69316.84 |
| 1951 | 79045 | 72759.02 |
| 1952 | 80485 | 76310.80 |
| 1953 | 83555 | 79969.69 |
| 1954 | 87045 | 83732.61 |
| 1955 | 91239 | 87595.81 |
| 1956 | 98085 | 91554.95 |
| 1957 | 101245 | 95605.03 |
| 1958 | 106215 | 99740.44 |
| 1959 | 114195 | 103954.96 |
| 1960 | 123983 | 108241.81 |
| 1961 | 131755 | 112593.64 |
| 1962 | 141195 | 117002.63 |
| 1963 | 152715 | 121460.47 |
| 1964 | 164185 | 125958.50 |
| 1965 | 174014 | 130487.72 |
| 1966 | 175765 | 135038.85 |
| 1967 | 184315 | 139602.46 |
| 1968 | 184815 | 144169.01 |
| 1969 | 181525 | 148728.91 |
| 1970 | 189726 | 153272.65 |
| 1971 | 190321 | 157790.85 |
| 1972 | 197829 | 162274.34 |
| 1973 | 208097 | 166714.24 |
| 1974 | 215564 | 171102.01 |
| 1975 | 215289 | 175429.54 |
| 1976 | 210740 | 179689.17 |
| 1977 | 199653 | 183873.77 |
| 1978 | 195844 | 187976.78 |
| 1979 | 194690 | 191992.21 |
| 1980 | 193143 | 195914.68 |
| 1981 | 192115 | 199739.46 |
| 1982 | 190767 | 203462.42 |
| 1983 | 192906 | 207080.09 |
| 1984 | 194180 | 210589.61 |
| 1985 | 197463 | 213988.72 |
| 1986 | 198299 | 217275.76 |
| 1987 | 204677 | 220449.63 |
| 1988 | 217589 | 223509.77 |
| 1989 | 222671 | 226456.09 |
| 1990 | 237657 | 229289.00 |
| 1991 | 247035 | 232009.32 |
| 1992 | 249615 | 234618.26 |
| 1993 | 250025 | 237117.38 |
| 1994 | 244385 | 239508.56 |
| 1995 | 248998 | 241793.95 |
| 1996 | 251725 | 243975.93 |
| 1997 | 247655 | 246057.10 |
| 1998 | 242807 | 248040.22 |
| 1999 | 240877 | 249928.18 |
| 2000 | 240659 | 251723.99 |
| 2001 | 241133 | 253430.75 |
| 2002 | 239246 | 255051.59 |
| 2003 | 241160 | 256589.69 |
| 2004 | 242300 | 258048.25 |
| 2005 | 245996 | 259430.45 |
| 2006 | 249029 | 260739.46 |
| 2007 | 255210 | 261978.41 |
| 2008 | 253556 | 263150.38 |
| 2009 | 244247 | 264258.39 |
| 2010 | 244593 | 265305.40 |
| 2011 | 246937 | 266294.30 |
| 2012 | 253788 | 267227.88 |
| 2013 | 260013 | 268108.86 |
| 2014 | 260097 | 268939.87 |
| 2015 | 269394 | 269723.44 |
| 2016 | 271996 | 270462.02 |
| 2017 | 275124 | 271157.95 |
| 2018 | 277670 | 271813.48 |
| 2019 | 271936 | 272430.78 |
| 2020 | 152084 | 273011.91 |
| 2021 | 170190 | 273558.84 |
| 2022 | 217509 | 274073.45 |
| 2023 | 248348 | 274557.55 |
Result_2 :
| $S_{max}$ | 282000 |
| $b$ | 0.0648 |
| $t_{i}$ | 1967.3060 |
| $R^{2}$ | 0.9650 |
Analysis_1 :
| Case | $S_{max}$ | $b$ | $t_{i}$ | $R^{2}$ |
| Including 2020-2023 | 284000 | 0.0597 | 1969.5467 | 0.9209 |
| Excluding 2020-2023 | 282000 | 0.0648 | 1967.3060 | 0.9650 |
The $R^{2}$ value improves significantly when excluding 2020-2023 data, increasing from 0.9209 to 0.9650, indicating a better model fit. The inflection year moves slightly earlier (from 1969.55 to 1967.31) when excluding 2020-2023 data. The estimated saturation level Smax​ is slightly lower (282,000 vs. 284,000) in the model without 2020-2023 data, suggesting that pandemic-related disruptions might have influenced the growth trajectory.

#### Curve Fitting for Passengers carried

* including 2020-2023 data:
1950-2023 Passengers Carried Data Table_1 :
| Year | Passengers Carried (Million) | Predicted Data (Million) |
| 1950 | 3095 | 4758.13 |
| 1955 | 3849 | 5185.17 |
| 1960 | 5124 | 5611.72 |
| 1965 | 6721 | 6031.99 |
| 1970 | 6534 | 6440.49 |
| 1971 | 6659 | 6520.34 |
| 1972 | 6724 | 6599.48 |
| 1973 | 6871 | 6677.89 |
| 1974 | 7113 | 6755.53 |
| 1975 | 7048 | 6832.37 |
| 1976 | 7180 | 6908.38 |
| 1977 | 7068 | 6983.53 |
| 1978 | 6997 | 7057.80 |
| 1979 | 6931 | 7131.16 |
| 1980 | 6825 | 7203.59 |
| 1981 | 6793 | 7275.06 |
| 1982 | 6742 | 7345.56 |
| 1983 | 6797 | 7415.07 |
| 1984 | 6884 | 7483.57 |
| 1985 | 6941 | 7551.05 |
| 1986 | 7104 | 7617.49 |
| 1987 | 7362 | 7682.89 |
| 1988 | 7767 | 7747.22 |
| 1989 | 7979 | 7810.48 |
| 1990 | 8358 | 7872.67 |
| 1991 | 8685 | 7933.77 |
| 1992 | 8825 | 7993.79 |
| 1993 | 8915 | 8052.71 |
| 1994 | 8885 | 8110.55 |
| 1995 | 8982 | 8167.28 |
| 1996 | 9005 | 8222.93 |
| 1997 | 8865 | 8277.48 |
| 2000 | 8671 | 8434.61 |
| 2005 | 8683 | 8675.10 |
| 2006 | 8778 | 8720.06 |
| 2007 | 8988 | 8763.99 |
| 2008 | 8984 | 8806.91 |
| 2009 | 8841 | 8848.83 |
| 2010 | 8818 | 8889.77 |
| 2011 | 8837 | 8929.72 |
| 2012 | 8963 | 8968.72 |
| 2013 | 9147 | 9006.76 |
| 2014 | 9088 | 9043.87 |
| 2015 | 9308 | 9080.06 |
| 2016 | 9392 | 9115.35 |
| 2017 | 9488 | 9149.74 |
| 2018 | 9556 | 9183.25 |
| 2019 | 9503 | 9215.91 |
| 2020 | 6707 | 9247.72 |
| 2021 | 7061 | 9278.70 |
| 2022 | 7885 | 9308.86 |
| 2023 | 9501 | 9338.23 |
Result_3 :
| $S_{max}$ | 10300 |
| $b$ | 0.0332 |
| $t_{i}$ | 1954.5890 |
| $R^{2}$ | 0.6764 |
* Excluding 2020-2023 data:
1950-2023 Passengers Carried Data Table_2 :
| Year | Passengers carried (Million) | Predicted Data (Million) |
| 1950 | 3095 | 3935.91 |
| 1955 | 3849 | 4504.25 |
| 1960 | 5124 | 5085.84 |
| 1965 | 6721 | 5665.11 |
| 1970 | 6534 | 6226.75 |
| 1971 | 6659 | 6335.73 |
| 1972 | 6724 | 6443.36 |
| 1973 | 6871 | 6549.55 |
| 1974 | 7113 | 6654.22 |
| 1975 | 7048 | 6757.28 |
| 1976 | 7180 | 6858.67 |
| 1977 | 7068 | 6958.31 |
| 1978 | 6997 | 7056.15 |
| 1979 | 6931 | 7152.14 |
| 1980 | 6825 | 7246.21 |
| 1981 | 6793 | 7338.33 |
| 1982 | 6742 | 7428.46 |
| 1983 | 6797 | 7516.58 |
| 1984 | 6884 | 7602.65 |
| 1985 | 6941 | 7686.65 |
| 1986 | 7104 | 7768.58 |
| 1987 | 7362 | 7848.41 |
| 1988 | 7767 | 7926.16 |
| 1989 | 7979 | 8001.80 |
| 1990 | 8358 | 8075.36 |
| 1991 | 8685 | 8146.84 |
| 1992 | 8825 | 8216.25 |
| 1993 | 8915 | 8283.61 |
| 1994 | 8885 | 8348.93 |
| 1995 | 8982 | 8412.25 |
| 1996 | 9005 | 8473.58 |
| 1997 | 8865 | 8532.96 |
| 2000 | 8671 | 8699.66 |
| 2005 | 8683 | 8941.60 |
| 2006 | 8778 | 8984.95 |
| 2007 | 8988 | 9026.72 |
| 2008 | 8984 | 9066.94 |
| 2009 | 8841 | 9105.67 |
| 2010 | 8818 | 9142.95 |
| 2011 | 8837 | 9178.81 |
| 2012 | 8963 | 9213.30 |
| 2013 | 9147 | 9246.46 |
| 2014 | 9088 | 9278.33 |
| 2015 | 9308 | 9308.96 |
| 2016 | 9392 | 9338.38 |
| 2017 | 9488 | 9366.63 |
| 2018 | 9556 | 9393.75 |
| 2019 | 9503 | 9419.78 |
| 2020 | 6707 | 9444.76 |
| 2021 | 7061 | 9468.73 |
| 2022 | 7885 | 9491.72 |
| 2023 | 9501 | 9513.76 |
Result_4 :
| $S_{max}$ | 10000 |
| $b$ | 0.0467 |
| $t_{i}$ | 1959.2640 |
| $R^{2}$ | 0.8961 |
Analysis_2 :
| Case | $S_{max}$ | $b$ | $t_{i}$ | $R^{2}$ |
| Including 2020-2023 | 10300 | 0.0332 | 1954.5890 | 0.6764 |
| Excluding 2020-2023 | 10000 | 0.0467 | 1959.2640 | 0.8961 |
The model including 2020-2023 data has a very low $R^{2}$ (0.6764), meaning it does not explain much variance in the data. This suggests that pandemic-related disruptions do not follow the logistic growth pattern. When excluding 2020-2023 data, $R^{2}$ improves significantly to 0.8961, meaning the model fits the historical trend much better. The inflection point shifts from 1954.59 to 1959.26, indicating a slightly later transition to the growth phase. The saturation level Smax​ is reduced from 10,300 to 10,000, showing that the long-term estimated carrying capacity is slightly lower when considering only stable historical trends.

### Analysis

Based on the logistic curve fitting results, we can identify the three life-cycle stages for Japan's railway system:
Birthing Phase (before $t_{i}$​):
* For passenger-km, the inflection year ($t_{i}$​) is around 1967-1969, indicating rapid initial growth from the 1950s onward.
* For passengers carried, the inflection year ($t_{i}$​) is 1954-1959, suggesting that ridership expansion occurred slightly earlier than total distance traveled.
Growth Phase (after $t_{i}$​ but before saturation):
* Strong growth continues through the 1970s and 1980s, corresponding to the expansion of the Shinkansen and modernization efforts.
Maturity Phase (approaching Smax​):
* The model suggests that the system started approaching saturation in the late 1990s and early 2000s.
* The impact of privatization (JNR → JR Group in 1987) may have influenced this transition.
* The COVID-19 pandemic (2020-2023) disrupted normal trends, making it difficult to fit a logistic model that accounts for recent years.
The logistic model assumes a smooth growth trajectory toward saturation. However, the COVID-19 pandemic caused an external shock, leading to a temporary decline in ridership and passenger-kilometres. The $R^{2}$ value significantly drops when including 2020-2023 data, suggesting that the pandemic years do not align with the logistic growth pattern. Excluding 2020-2023 provides a more accurate model for the long-term historical trend of Japan's railway development.

## References

* ↑ MIZUTANI, F., & NAKAMURA, K. (1997). PRIVATIZATION OF THE JAPAN NATIONAL RAILWAY: OVERVIEW OF PERFORMANCE CHANGES. International Journal of Transport Economics / Rivista Internazionale Di Economia Dei Trasporti, 24(1), 75–99. http://www.jstor.org/stable/42747282
* 1 2 3 Smith, I. B. (1996). The privatisation of the JNR in historical perspective: An evaluation of government policy on the operation of the national railways in Japan [Doctoral dissertation, University of Stirling]. University of Stirling Repository. https://hdl.handle.net/1893/29273
* ↑ Ishino, T., et al. (Eds.). (1998). 停車場変遷大事典 国鉄・JR編 [Station transition directory – JNR/JR] (Vol. I). JTB Corporation.
* ↑ Kakumoto, Ryohei. "Sensible Politics and Transport Theories? —Japan’s National Railways in the 20th Century." Japan Railway & Transport Review 22 (1999): 22–33.
* 1 2 3 4 Ministry of Land, Infrastructure, Transport and Tourism. (2023). Annual report on transport policy: White paper on transport (Reiwa 5 [2023]). Ministry of Land, Infrastructure, Transport and Tourism. https://www.mlit.go.jp/sogoseisaku/transport/sosei_transport_fr_000164.html
* 1 2 3 4 Statistics Bureau of Japan. (2011-2025). Japan Statistical Yearbook. Statistics Bureau, Ministry of Internal Affairs and Communications. Available at: Japan Statistical Yearbook
* 1 2 3 4 Statistics Bureau of Japan. (2023). Population statistics. e-Stat, Government of Japan. Available at: e-Stat Japan
* 1 2 3 4 Ministry of Land, Infrastructure, Transport and Tourism. (1974-1998). White paper on transport in Japan. Ministry of Land, Infrastructure, Transport and Tourism. https://www.mlit.go.jp/english/white-paper/unyu-whitepaper/index.html